Objective. All motor commands flow through motoneurons, which entrain control of their innervated muscle fibers, forming a motor unit (MU). Owing to the high fidelity of action potentials within MUs, their discharge profiles detail the organization of ionotropic excitatory/inhibitory as well as metabotropic neuromodulatory commands to motoneurons. Neuromodulatory inputs  enhance motoneuron excitability and facilitate persistent inward currents (PICs). PICs introduce quantifiable properties in MU discharge profiles by augmenting depolarizing currents upon activation (i.e. PIC amplification) and facilitating discharge at lower levels of excitatory input than required for recruitment (i.e. PIC prolongation). Approach. Here, we introduce a novel geometric approach to estimate neuromodulatory and inhibitory contributions to MU discharge by exploiting discharge non-linearities introduced by PIC amplification during time-varying linear tasks. In specific, we quantify the deviation from linear discharge (‘brace height’) and the rate of change in discharge . We further characterize these metrics on a simulated motoneuron pool with known excitatory, inhibitory, and neuromodulatory inputs and on human MUs . Main results. In the simulated motor pool, we found brace height and attenuation slope to consistently indicate changes in neuromodulation and the pattern of inhibition (excitation–inhibition coupling), respectively, whereas the paired MU analysis (ΔF) was dependent on both neuromodulation and inhibition pattern. Furthermore, we provide estimates of these metrics in human MUs and show comparable variability in ΔF and brace height measures for MUs matched across multiple trials. Significance. Spanning both datasets, we found brace height quantification to provide an intuitive method for achieving graded estimates of neuromodulatory and inhibitory drive to individual MUs. This complements common techniques and provides an avenue for decoupling changes in the level of neuromodulatory and pattern of inhibitory motor commands. In this paradigm, the reporter MU serves as a proxy for synaptic drive and is used to estimate the difference in excitatory input required at recruitment and de-recruitment of the test MU . Several groups have made notable efforts to quantify this non-linearity by fitting ascending phases of MU discharge with either an exponential or linear function and dichotomizing MUs based on fit error  from a theoretical linear increase in discharge rate, from recruitment to peak, can be used as a proxy for PIC amplification. Furthermore, given that this maximum deviation is geometrically the point at which the change in discharge rate over time transitions from a trend of steep increase to an attenuated increase, this point can be used to separate and characterize the secondary and tertiary discharge ranges  relations recorded during somatic voltage-clamp and the frequency–current (F–I) relations recorded during somatic current injection. The range of observed behaviors was achieved by systematically varying several parameters across the pool, including AHP duration, specific input resistance, surface area, and the density and half-activation voltage of the PIC conductance.Simulated motoneuron spike trains were obtained from a pool of motoneurons modeled with methods similar to those discussed previously . Second, AHP durations and amplitudes were increased by increasing the values of the time constant of calcium removal for the calcium-activated potassium conductance . The larger and longer AHPs acted to oppose early PIC activation during increasing excitatory synaptic drive, so we hyperpolarized the PIC half-activation threshold . Finally, in the original model, the slow decay of PICs observed in high threshold MG motoneurons . Neuromodulatory inputs were adjusted by multiplying the max conductance of the L-type calcium channel by a multiplier  to either increase or decrease the relative magnitude of PICs. The pattern of inhibition was adjusted as a function of excitation through dendritic inhibitory conductance. Specifically, inhibitory conductance was adjusted to recreate an inhibitory input that positively scaled with the excitatory input  or an inhibitory input that negatively scaled with excitatory input . For all simulations, models were driven with noisy (Gaussian) excitatory conductance commands that increased and decreased, reaching peak conductance in 10 s, as described previously  in the lower limb cohort and nine participants  in the upper limb cohort. Participants reported no known neuromuscular, musculoskeletal, or cardiovascular impairments and provided written and informed consent in accordance with the Declaration of Helsinki. Portions of the data collected in the lower limb cohort have been previously reported elsewhere and are used here as a secondary analysis , with their ankle joint coincident to the axis of rotation of the dynamometer, their hips at approximately 80° of flexion, their left knee at 20° flexion, and their left ankle at 10° of plantarflexion. In the upper limb cohort, a participant’s left forearm was cast and fixed to a rigid horizontal aluminum plate while their left index finger was held parallel to their forearm and placed perpendicular to a linear load cell  at the proximal interphalangeal joint. Throughout the experimental session, we maintained a participant’s upper limb in 90° of elbow flexion, 40° of shoulder flexion, and 80° of shoulder abduction. Target effort ramps (i.e. dorsiflexion/plantarflexion torque or index finger abduction force) were provided on a television screen via a custom interface (MATLAB (R2020b), The Mathworks Inc., Natick, MA), with collected torque/force smoothed by a 125 ms moving average window before being provided as visual feedback to the participant. For subsequent analysis, raw signals were digitized (2048 Hz) and lowpass filtered (50 Hz) with a fifth-order Butterworth filter.2.2.4.At the onset of each experimental session, we asked participants to first generate maximal voluntary isometric contractions in the direction of interest for that given session. This included either maximum dorsiflexion and plantarflexion or maximum index finger abduction. A minimum of two maximal contractions were performed, with at least 1 min of rest separating contractions, and repeated until the peak torque/force within the last contraction deviated by less than 10% of the previous contraction. We then used the maximum voluntary torque/force (MVT/F) achieved during these contractions to normalize all subsequent ramp contractions. Each ramp contraction started from rest and consisted of a 10 s linear increase to 30% MVT and a 10 s decrease back to rest (i.e. 3% MVT/s rise and decay speeds). To mitigate potential learning effects and ensure smooth contractions, participants completed a minimum of six practice ramps. Following practice trials, each experimental session consisted of 4–12 ramp contractions for each target direction.2.2.5.In the lower limb, high-density surface EMG (HD-sEMG) was collected via 64 channel electrode grids  placed atop the skin overlying the TA, MG, and SOL muscle bellies with adhesive foam and conductive paste. In the upper limb cohort, HD-sEMG was similarly collected via 64 channel electrode grids  placed atop the skin overlying the FDI. Before electrode placement, the muscles of interest were identified by experienced investigators and the skin overlying the muscle was shaved, abraded with abrasive paste, and cleaned with isopropyl alcohol. In the lower limb cohort, two Ag/AgCl ground electrodes were placed bilaterally on the right and left patella and a moist band electrode was placed around the right ankle. In the upper limb cohort, one Ag/AgCl electrode was placed on the left acromion. All HD-sEMG signals were acquired with differential amplification (150×), bandpass filtered (10–900 Hz), and digitized (2048 Hz) using a 16-bit analog-to-digital converter .et alet alet alet alet alFollowing collection, each channel of surface EMG was bandpass filtered at 20–500 Hz  and visually inspected to remove channels with substantial artifacts, noise, or saturation of the A/D board. The remaining EMG channels were decomposed into individual MU spike trains using convolutive blind source separation and successive sparse deflation improvements , The Mathworks Inc., Natick, MA). Smooth estimates of discharge rate were then generated with Matlab’s inbuilt predict function, along a time vector from MU recruitment to derecruitment sampled at 2048 Hz (MATLAB (R2020b), The Mathworks Inc., Natick, MA). Hyperparameters were chosen in accordance with those previously suggested  to create a continuous estimate of discharge rate for each MU, as previously described  is quantified as the change in discharge rate of a lower threshold MU (reporter unit) between the recruitment and derecruitment instance of the test unit. To account for the possible pairing of a test unit with multiple lower threshold reporter units, we represented ΔF for a given test unit as the average change in discharge rate across all reporter unit pairs. To ensure that MU pairs likely received a common synaptic drive, we only included test unit-reporter unit pairs with rate-rate correlations of r2 > 0.7  with spike triggered averaging and computed a 2D cross correlation between the spatial representation of the MUAPs between ramp contraction trials , neuromodulation , inhibition shape , and their interaction. To observe the magnitude of change between either neuromodulation or inhibition patterns, we computed estimated marginal mean differences between factor levels. The progression of these relations across MUs of varying recruitment thresholds was further appreciated by segmenting the data along recruitment thresholds  as shown in figure To compare their ability for detecting potential changes in neuromodulatory or inhibitory inputs to MUs, we quantified brace height, its supplemental metrics, and ΔF in human MUs, we quantified all metrics on the collected human dataset . We fit a linear mixed effects model to each metric, consisting of muscle  and torque at recruitment (% MVT) as fixed effects and participant as a random effect. We then used estimated marginal means to compare the magnitude of difference between muscles for each metric. To observe the variability of each metric across repeated observations of the same unit, replicates of identical MUs were identified with a 2D cross correlation of MUAPs, as described, and the metrics for matched MUs were compared. To compare the variability, we quantified the coefficient of variation for each metric within a collection of matched MUs and predicted this with a linear mixed effect model consisting of metric  as a fixed effect and participant as a random effect.To compare the similarity of brace height, its supplemental metrics, and Δp-values were obtained by likelihood ratio tests of the full model with the effect in question against the model without the effect in question. For main effects, this included their subsequent interaction terms. To ensure the validity of model fitting, the assumptions of linearity and normal, homoscedastic residual distributions were confirmed. When appropriate, dependent variables were log-transformed and estimated marginal means back-transformed. Explicitly, predictions of acceleration slope were found to produce non-linear residuals and model predicted data that did not replicate the skewed distribution observed in acceleration slope and was remedied by log-transforming the dependent variable. Estimated marginal means were employed in pairwise post-hoc testing and achieved with the emmeans package , while tighter spacing along the y-axis indicates lower sensitivity to varying inhibitory patterns . As detailed in section F = 3.00, p = 0.001), post-hoc testing reveals no significant differences between sequential increases in inhibitory pattern. Additionally, brace height appears insensitive to relatively large changes in inhibitory pattern, with estimates of brace height not significantly different from zero between strong reciprocal inhibition (−0.7) and constant inhibition (0) , or between constant inhibition (0) and strong proportional inhibition (0.7) .Across sequentially greater levels of simulated neuromodulation, we observed brace height to increase similarly for each level of inhibition . In contrast to brace height, we found that neuromodulation was not a significant predictor of ΔF changes at lower levels of neuromodulation , whereas, at higher levels of neuromodulation, ΔF is estimated to significantly increase by 1.75 pps , 3.85 pps , and 2.47 pps  for each increase in neuromodulation level from 0.9–1.0, 1.0–1.1, and 1.1–1.2, respectively. Interestingly, ΔF exhibits an interaction between neuromodulation and inhibitory shape  with differences in ΔF between neuromodulation levels greater under reciprocal inhibition . Averaging across levels of neuromodulation, we found ΔF to significantly decrease by 1.96 pps  between strong reciprocal inhibition (−0.7) and constant inhibition (0), and by 2.39 pps  between constant inhibition (0) and strong proportional inhibition (0.7).Similar to brace height, we found neuromodulation to be a significant predictor of ΔF and brace height estimates into cohorts based on their recruitment threshold  and angle , but not attenuation slope . Acceleration slope, averaging across levels of inhibition, is estimated to significantly increase by 0.67 pps s−1 , 1.43 pps s−1 , and 1.15 pps s−1  for increases in neuromodulation level from 0.9–1.0, 1.0–1.1, and 1.1–1.2, respectively. Conversely, angle is estimated to significantly increase by 4.60°  and 6.55°  for increases in neuromodulation level from 0.8–0.9 and 0.9–1.0, respectively.For the additional metrics garnered from brace height quantification , acceleration slope , and angle . Attenuation slope is estimated to significantly increase by 0.50 pps s−1  from proportional inhibition (0.7) to constant inhibition (0), with no significant changes from strong reciprocal (−0.7) to constant (0) but an estimated decrease of 0.71 pps s−1  from strong reciprocal (−0.7) to strong proportional (0.7). We found acceleration slope to significantly increase by 0.32 pps s−1  from proportional inhibition (0.7) to constant inhibition (0), with no significant changes from strong reciprocal (−0.7) to constant (0) but an estimated decrease of 0.37 pps s−1  from strong reciprocal (−0.7) to strong proportional (0.7). Similarly, we found angle to significantly increase by 7.03°  from proportional inhibition (0.7) to constant inhibition (0), with no significant changes from strong reciprocal (−0.7) to constant (0) but an estimated decrease of 9.39°  from strong reciprocal (−0.7) to strong proportional (0.7).We found inhibition pattern to be a significant predictor of attenuation slope . These values can be seen for ΔF and brace height in figure F  than for brace height, acceleration slope, attenuation slope, and angle .To observe the behavior of brace height, its supplemental metrics, and ΔF and normalized brace height, we found muscle (ΔF: ; brace height: ), torque at MU recruitment (ΔF: ; brace height: ), and their interaction (ΔF: ; brace height: ) to be significant predictors. Estimated marginal means of ΔF and brace height for each muscle are shown in table F is estimated to be significantly different between all but the FDI and TA. Expanding estimates across the recruitment threshold of MUs, distinct relationships can be observed for ΔF and brace height. As seen in figure F and brace height decrease with MUs recruited at higher levels of torque. Traversing from 1% MVT to 30% MVT, averaging across muscles, ΔF is estimated to decrease by 0.018 pps  and brace height is estimated to decrease by 0.40% rTri . Though significant differences are observed between muscles and across recruitment threshold, the effects are noticeably small and may possess no functional relevance.For both Δχ2(1) = 613.64, p < 0.001]; attenuation: ) and their interaction (acceleration: ; attenuation: ) to be significant predictors. In contrast, for angle, we found recruitment threshold to fail as a significant predictor but muscle (χ2(3) = 127.32, p < 0.001) and their interaction (χ2(3) = 49.22, p < 0.001) to be significant predictors. Marginal means for each metric and each muscle averaged across recruitment thresholds can be seen in table For both acceleration and attenuation slope, we found muscle to fail as a significant predictor but torque at recruitment  is shown on the right of figure F values, which can be observed with the dispersion of values across reporter units in the right plot of figure To observe the variability of each metric across repeated observations of the same unit, replicates of identical TA MUs were identified in repeated ramp contractions, as described, yielding a total of 158 unique MUs with an average of 3.30 SD: 1.63) occurrences. An example of a single unique MU can be seen in figure 3 occurreF while 142 MUs were estimated with brace height and its associated metrics. This created an average number of repeated unit observations of 2.34 (SD: 1.82) for ΔF and 2.91 (SD: 1.74) for brace height. Fitting a linear mixed model to the coefficient of variation of metric estimates for replicates of each unique MU, we found the type of metric to be a significant predictor (χ2(5) = 373, p < 0.001). Marginal means for the coefficient of variation are estimated as 0.120  for ΔF, 0.138  for brace height, 0.318  for acceleration slope, 0.245  for attenuation slope, 0.0277  for angle, and 0.129  for torque at recruitment. Of note, all pairwise comparisons between metric estimates are predicted significantly different (p < 0.0001), except the differences between ΔF and brace height, ΔF and torque at recruitment, brace height and torque at recruitment, and between acceleration and attenuation slope.Across 158 unique MUs, 135 MUs yielded valid estimates for Δ4.F) on a collection of human MUs and investigate their ability to detect changes in neuromodulatory and inhibitory drive to a simulated pool of motoneurons with known inputs.As a result of the high fidelity of action potentials within a MU, their discharge patterns provide a unique window into the composition of excitatory, inhibitory, and neuromodulatory commands to motoneurons. In the present study, we present a complementary approach to the most commonly used methods for quantifying the contributions of these commands to the discharge profile of human MUs. In particular, we propose quantifying discharge rate nonlinearity following the onset of MU recruitment during time varying linear tasks, as this nonlinearity is commonly attributed to neuromodulatory commands and believed indicative of intrinsic activation by monoaminergic dependent PICs (i.e. PIC amplification). We term this nonlinearity brace height and use its instance of occurrence to garner additional metrics  for characterizing the secondary and tertiary range of MU discharge. We then compare these metrics to the commonly used paired MU analysis . Though similar in magnitude, the greater consistency of brace height across neuromodulation levels can be appreciated in figures F displays a wider range of sensitivity between neuromodulation values and proves insensitive to neuromodulation changes between the lower spectrum of values (0.8–0.9). Similarly, values of acceleration slope and angle show inconsistent sensitivity to changes in neuromodulation, with no predicted changes for acceleration slope at lower values of neuromodulation and for angle at higher values of neuromodulation  could also affect ΔF estimates and may confound interpretations of hysteresis. Specifically, given that units are paired across recruitment thresholds to obtain ΔF estimates, changes in ΔF cannot be isolated to alterations in either the discharge profile of the reporter unit or hysteresis of the test unit, making interpretations across recruitment threshold difficult. To overcome this potential limitation, we have employed a test-unit average scheme and various control conditions to find suitable reporter units  to strong proportional inhibition (0.7) in four of the five neuromodulation levels . Averaging across all neuromodulation levels yields a standardized mean difference of 0.721  from strong reciprocal to proportional inhibition. It is speculated that inhibitory-driven changes in rate modulation are responsible for these observed differences, as suggested previously  from strong reciprocal (−0.7) to proportional inhibition (0.7). That said, ΔF appears to be largely insensitive to changes in neuromodulation and inhibition profile when neuromodulation values are lower. This may be due to the bias inhibition employed in our simulations. A constant bias inhibitory offset to the motoneuron pool was employed in all simulations and is necessary to ensure the simulated motoneurons cease discharge at the cessation of excitatory input. As ΔF quantifies discharge rate hysteresis, the employed neuromodulation parameters were likely insufficient to generate any appreciable hysteresis at lower values and thus produced the observed floor effect. Regardless, a significant interaction is still observed and implies that changes in ΔF are greatest at both higher levels of neuromodulation and greater reciprocal inhibition (−0.7), and are attenuated under proportional inhibition (0.7) and at lower neuromodulation levels.In addition to changes in attenuation slope, altering the pattern of inhibitory input revealed a distinct interaction between inhibition and neuromodulation in Δ4.1.3.F and brace height respond similarly to changes in the neuromodulatory commands to motoneurons, they differ in their response to changes in inhibitory commands, thus providing complementary information. The absence of a strong relation to the pattern of inhibitory input may allow brace height to be a more focal quantifier of neuromodulatory commands and allow for inhibitory and neuromodulatory driven changes in ΔF to be decoupled. Furthermore, the additional metrics achieved with brace height quantification may assist in this decoupling process and provide a more granular view of changes in MU discharge patterns. Specifically, as indicated by the simulation results, the attenuation slope appears to be a consistent quantifier of the pattern of inhibitory input while the acceleration slope and angle may additionally indicate changes in neuromodulation. Exploiting these relations and using brace height and attenuation slope to estimate neuromodulatory and inhibitory commands, respectively, may provide a greater potential for understanding the synaptic organization of motor commands to the motor pool.In total, although Δ4.2.F, brace height, acceleration slope, attenuation slope, and angle values can be seen for each muscle in table F generated reduced estimates  when compared to brace height, attenuation slope, acceleration slope, and angle .Estimated average ΔF and brace height, we found muscle to be a significant predictor, with brace height significantly different between all muscles except FDI and SOL and ΔF significantly different between all but FDI and TA. Of note, though estimated as significantly different, the distributions indicated in figure F, we found a greater relative decrease in brace height with increasing values of recruitment threshold  this observation can likely be attributed to a greater acceleration slope relative to attenuation slope in the TA (i.e. acceleration/attenuation slope ratio). In general, we theorize that a greater acceleration slope would indicate a MU that has quickly turned on, likely as a result of intrinsic activation from PICs  observations for ΔF and 142 MUs with an average of 2.91 (SD: 1.74) observations for brace height, acceleration slope, attenuation slope, and angle. Across MUs, we did not find the coefficient of variation in measurements for ΔF and brace height to be significantly different from those seen with the torque at which a MU was recruited. This indicates that the inherent variability of the MU, or our ability to estimate its recruitment time, is no less than that of ΔF and brace height and implies that the variation in these values could be due to the variability of the MU. Of note, angle proved remarkably stable with a significantly lower coefficient of variation than all other metrics, including torque at MU recruitment. This is likely an artifact of its considerably higher average values.Comparing this variability systematically across all unique MUs, we found brace height and Δ4.4.F could also be due to changes in excitation–inhibition coupling. Additionally, one may question the practicality of the range of neuromodulatory parameters employed in the simulations. As detailed, changes in neuromodulatory command were achieved by adjusting the maximum conductance of the L-type Ca channels that simulate intrinsic activation by PICs. Such an approach has been thoroughly characterized previously and will not be belabored here . Such a dynamic change in inhibitory pattern to the motor pool may be highly speculative at this point, though recent work could point to potential mechanisms  may not be thoroughly characterized. To overcome this limitation, a third region could be defined from peak discharge to peak torque and its slope quantified. This may provide additional information for units that more frequently display this phenomenon during triangular ramp contractions . Lastly, more rigorous exclusion criteria may be warranted to better isolate changes in neuromodulation or inhibitory profile and remove erroneous estimates.Additional effort that expands upon the work presented here is certainly warranted. In specific, additional investigation utilizing biasing offsets of inhibition is warranted to both investigate the effects of tonic inhibitory input on the proposed metrics and alleviate the floor effect observed with Δ5.F) to depend on both neuromodulation and the pattern of inhibition supplied to the motoneuron pool. Spanning both the simulated motoneurons and human MUs, the quantification of brace height supplies an intuitive and computationally inexpensive method for achieving graded estimates of neuromodulatory and inhibitory drive to MUs on a single unit level. Using brace height and its associated metrics provides a complementary view to the commonly used paired analysis technique and generates a potential avenue for decoupling changes in neuromodulatory drive and the profile of inhibitory input (excitation–inhibition coupling).In the present study, we employ a geometric approach for estimating neuromodulatory and inhibitory contributions to motoneuron discharge, by exploiting discharge non-linearities introduced by PICs. In specific, we detail methods for characterizing the ascending phase of MU discharge during time-varying linear tasks, including the deviation from linear discharge (i.e. brace height) and the rate of change in discharge . We further characterize these metrics on a large human MU dataset and a simulated motoneuron pool with known excitatory, inhibitory, and neuromodulatory inputs. Using known inputs, we found brace height and attenuation slope to consistently represent neuromodulation and the pattern of inhibition to the motoneuron pool, respectively. In contrast, we found the paired MU analysis technique (Δ


In the present investigation, yam mucilage was evaluated as a stabilizer and emulsifier in the formulation of vanilla flavored ice cream; physicochemical, rheological, and stability characteristics were determined. A completely randomized bifactorial design was used . Results showed an increase in the protein content present in ice cream mixture as the amount of mucilage increases. Rheologically, it was found that ice cream has the characteristic behavior of a pseudoplastic fluid, presenting a viscoelastic structure where elastic behavior predominates. In addition, ratios with a higher content of mucilage incorporated a greater volume of air and presented the longest melting times, delaying drops falling time; in the same way mucilage gives ice cream a freezing temperature between −6.1 to −2.8 °C, indicating that the application of mucilage in food industry is possible due to its nutritional value, and it gives ice cream stability properties. Ice cream is a liquid mixture that after agitation and simultaneous cooling is transformed into a paste , it is aDuring the last two decades, the science and technology of ice cream has experienced a remarkable progress in terms of the exploration and understanding of factors such as structure, texture and stability during storage, which has led to the realization of the incorporation of new additives in formulations, not only because they provide modifications for particular requirements such as viscosity increase, emulsification, and water retention, but also to improve health-related aspects ,5,6. AmoDioscorea spp.) has been identified as one of the most important food crops and is a multi-species of over 600 species with subterranean tubers or rhizomes that grow in tropical and sub-tropical regions. The bioactive components of yam include mucilage polysaccharides (non-starch polysaccharides), starch, amino acids, flavonoids, allantoin, phenolic compounds, and other constituents. Presently, mucilage polysaccharides, as one of the major components of water-extracts from yam, are attracting increasing attention for their diverse bioactivity [Yam  from the department of Sucre (Colombia) was used. Bubbling method was used for the extraction of mucilage, done with continuous pilot-scale equipment located at Unitary Operations plant of the Universidad de Sucre, which operates in environmental conditions with a yam:water ratio of 1:8 and whose basis is flotation in the presence of air. Liquid mucilage was dried in a vacuum freeze dryer  for 60 h; for this purpose it was first frozen at −50 °C, and after drying it was macerated for later use [Yam . The equipment was controlled using RheoCompass software and data were processed using Microsoft Excel 2010.−1 in ascending and then in descending form of 100 to 0.1 s−1. The maximum shear gradient (100 s−1) was maintained for two minutes. The test was performed at a temperature of 25 °C. Experimental values of flow curves were adjusted to Oswald–de Waele (Equation (1)), Bingham (Equation (2)), and Herschel–Bulkley (Equation (3)) rheological models [−1), n is flow behavior index, K is consistency coefficient (Pa·sn), and σ0 is yield stress (Pa).For stationary tests, 10 mL of the ice cream mixture was taken and placed in a rheometer with cylinder attachment, varying the shear gradient in a continuous ramp from 0.1 to 100 sl models ,22.σ = K’, K”, n’, n” are constants and ω is the angular frequency, Hz.For dynamic tests, the viscoelastic behavior was determined operating in oscillatory mode, using parallel-plate geometry with a diameter of 25 mm . A gap of 1 mm between plates was fixed. Initially, an amplitude sweep was performed at a strain between 0–100% and frequency of 1.0 Hz, to determine the linear viscoelasticity region. The viscoelastic characterization of ice cream mixture was obtained by varying the frequency between 0.01 to 100 Hz , and rheOverrun: it was measured as described by Daw and Hartel , taking g was taken. Each sample was placed in a 2 mm wide square openings sieve, which was suspended on a previously tared beaker. Total melting time of the ice cream was recorded and weight was taken every 5 min, controlling the temperature between 24–25 °C by means of an air conditioning system [Melting: Based on samples of hardened ice cream in storage (approximately −15 °C), an approximate mass of 45 ± 5 Freezing point: it was determined using a cryoscope  according to the methodology reported by Marshall and Goff . The icep < 0.05. Data analysis were completed using R software version 3.1.2.A completely bifactorial random design was carried out where the first factor was yam mucilage:CMC ratio (M:CMC) with the following levels , and the second factor was the concentration of stabilizer (CS) with the following levels (0.4 and 0.8%) and the following dependent variables: flow behavior index, consistency coefficient, storage and loss modulus, loss tangent, melting, freezing point and overrun. Analysis of variance (ANOVA) and Tukey’s test were used to compare means. Differences were considered significant at a level of p < 0.001) significantly affected pH. At low stabilizer concentration (0.4%) pH does not change in different M: CMC ratios; however, there is a slight change with an CS of 0.8. Figures found that degradation to lactose and subsequent production of lactic acid are likely to happen.In p = 0.0002) had a significant effect on ST. High levels of concentration (0.8%) in different ratios do not affect TS, but at a low level of CS (0.4%) it shows differences, the M:CMC ratio being 80:20, which is why they would probably present smaller crystals in ice cream, because TS has to do with the size of ice crystals, since the smaller their amount, the larger their size [Total solid (TS) values ranged between 32.07 and 33.57%, which comply with Colombian regulations NTC, 1239). Similar results have been reported for ice cream with probiotic bacteria 36.2% , and in 9. Similaeir size .p = 0.0087) presented a significant effect on titratable acidity. With the increase of CS there is a decrease in the ice cream mixture titratable acidity. Acidity content is of vital importance, which should not be too low, since it can cause the precipitation of casein [Titratable acidity fluctuated between 0.028 and 0.084%, similar values have been found by Kavaz and Yüksel  that repf casein , but toop = 0.0035) and M: CMC interaction (p = 0. 0074) had a significant effect on ashes. It was found that for CS values of 0.4% there is no change in the different ratios, while for EC values of 0.8% with the decrease of mucilage content in M: CMC ratio there is an increase in the amount of ash in the mixture. The treatment with a 20:80 ratio and 0.8% of stabilizer, was the one with the highest ash content. Increasing the concentration decreases free water activity by increasing ash content, which is beneficial for the ice cream as ashes have physiological importance, since they participate in taste, inhibit enzymatic catalysis and other reactions that influence food texture [Ash values were between 0.96 and 1.12%. Results similar to those were found in ice cream prepared using modified wheat gluten, 0.92% . Accordi texture .Fat values were between 6.77 and 7.70%, which are within the ranges allowed by Colombian regulations . Data similar to that reported for soft serve ice cream, 8% , but lowp = 0.0007) and CS (p = 0.0059) showed a significant effect on the protein. At an CS of 0.4%, no difference was found for protein, but as the M:CMC ratio increased, at high concentration, protein present in the ice cream mixture increased. The high values of proteins present in the ice cream mix in addition to improving the nutritional composition, helps ice cream stability as they emulsify fat and contribute to the formation of structures, including emulsification, shake and water retention capacity. Being very important for the correct development of the desired texture in the final product. Protein solubility and their interaction with other components of the mixture (polysaccharides such as mucilage) affected the ability to emulsify and stabilize fat globules that will be dispersed in this phase, to later crystallize them during the churning–freezing and stabilize at once the built-in air [Protein values were between 2.68 and 3.96%, which comply with Colombian regulations for ice cream , also agrees with values reported for different ice creams between 3 and 4% ,25. Accot-in air ,32,33.In 2aj. > 0.994). The flow behavior index (n) ranged between 0.471 and 0.995 . The flow behavior index increases with the decrease of CS and with the content of yam mucilage in the M: CMC ratio  and consistency coefficient (MC ratio . This red medium .G’) and loss module (G”), which were plotted as a function of frequency (Hz) (G’ ≥ G”), so that the weak gel behavior prevails except for the mixtures with 100:0 M:CMC ratio with 0.4 and 0.8% CS, they tend to be a structured liquid, but with high frequencies all treatments tend to form a strong gel (G’ < G”). Similar results have been found in the effect of basil seed, cress seed and quince seed gums on ice cream rheological properties, where G’ > G’’, predominantly the weak gel behavior [The main parameters for viscoelastically characterizing fluids are storage module (ncy (Hz) a,b. For behavior  and in mbehavior ,40.2 ranging between 75 and 92% (Equations (4) and (5)). The estimated values of parameters are summarized in Frequency sweeps results were analyzed using the power law model that showed an RK’ and K” ranged between 0.755–2.201 Pa·sn’ and 0.131–0.907 Pa·sn”, respectively, and the values of n’ and n” ranged from 2.062–2.264 to 1746–2201, respectively  and M:CMC ratio (p < 0.001) significantly affected the overrun variable. When CS (0.4 to 0.8%) and M:CMC ratio increase, an increase in the overrun or amount of air incorporated occurs. The incorporation of air in the ice cream is an important physical characteristic that affects its quality, interfering in texture, smoothness and stability, and this is introduced by the shake and is a necessary ingredient, because without it, it would be too dense, hard, and cold. Furthermore, the incorporation of air depends on the composition of the mixture (fat content), as well as on the type and amount of stabilizer and emulsifier used, so that yam mucilage gives the ice cream stability properties, and this is due to protein–fat interactions, since in these interactions hydrocolloids with a high protein content such as mucilage are key to the incorporation of air and its control, because proteins, due to their emulsifying properties during the shake, contribute to the formation of air bubbles [Results obtained for physical stability  of vanilla flavored ice cream are shown in Freezing point values were between −6.1 and −2.8 °C. Similar results are reported for coconut milk-based ice cream with inulin and locust bean gum addition, −3.0 and −2.5 °C . All facp < 0.001) and interaction CS and M: CMC ratio (p = 0143) presented a significant effect on the melting time variable. At a high level of concentration (0.8%) the increase in the amount of mucilage in M:CMC ratio produces an increase of more than 100% in melting time; however, at a low level of concentration (0.4%) this change is not that noticeable. Mucilage has an emulsifying capacity, which gives it fat stability and retention of air bubbles, factors that decrease the melting rate due to fat and air networks within the matrix that offer high resistance to the already existing fluid, which has melted by the transfer of heat with the environment.Melting time values were between 33.33 and 70 min. According to ANOVA, M:CMC ratio (The content of mucilage contributes to the increase of protein and ash content, and rheological analysis showed that mixtures of soft serve ice cream with addition of mucilage had a characteristic behavior of pseudoplastic fluid, at low frequencies the elastic behavior predominates over the viscous, except mixtures with 100% mucilage for both levels of working concentration that tend to be a structured liquid. Sweep frequency analysis showed that mixtures behaved as weak gels, and that the elastic component is more frequency dependent than the viscous component.Overrun, freezing point and melting, properties of physical stability, showed significant variation due to the interaction of factors, formulations with the highest mucilage content for both levels of concentration were those with the highest volume of air incorporated, melting and delay in the falling time of drops.


Milk fat is mainly composed of triglycerides, which are synthesized in the endoplasmic reticulum of mammary epithelial cells. Many uncertain regulatory factors and mechanisms are involved in this process. In this study, we found that inhibition of the Kirsten rat sarcoma viral oncogene homolog changed the lipid composition by regulating mitochondrial functions, autophagy, endoplasmic reticulum stress, and mitogen-activated protein kinase in a dairy cow mammary epithelial cell line. These results are helpful for exploring the synthesis and secretion of milk fat at the molecular level and providing a theoretical basis for improving the flavor and nutrition of milk.KRAS), or guanosine triphosphatase KRAS, is a proto-oncogene that encodes the small guanosine triphosphatase transductor protein. Previous studies have found that KRAS can promote cytokine secretion, cell chemotaxis, and survival. However, its effects on milk fat synthesis in bovine mammary epithelial cells are unclear. In this study, the effects of KRAS inhibition on cell metabolism, autophagy, oxidative stress, endoplasmic reticulum stress, mitochondrial function, and lipid composition as well as the potential mechanisms were detected in an immortalized dairy cow mammary epithelial cell line (MAC-T). The results showed that inhibition of KRAS changed the lipid composition , mitochondrial functions, autophagy, and endoplasmic reticulum stress in cells. Moreover, KRAS inhibition regulated the levels of the mammalian target of rapamycin and mitogen-activated protein kinase  activation. These results indicated that regulation of KRAS would affect the synthesis and composition of milk fat. These results are also helpful for exploring the synthesis and secretion of milk fat at the molecular level and provide a theoretical basis for improving the percentage of fat in milk and the yield of milk from cows.Kirsten rat sarcoma viral oncogene homolog ( Among the livestock species that produce milk around the world, cows have always been the dominant species. Milk is rich in nutrition, and in addition to providing the essential nutrients for the growth and development of newborns, milk contains all the amino acids required by humans and an abundance of minerals, vitamins, milk protein, and milk fat . Milk isThe synthesis of milk fat by bovine MECs (BMECs) is associated with many important processes, such as fatty acid (FA) transport, de novo FA synthesis, FA channeling and metabolism, and lipid droplet formation . CD36 isAfter lipid droplets are formed, the milk fat globule membrane (MFGM) can stabilize the structure of the droplet . PhosphoKRAS) has previously been found to promote cell survival, cytokine secretion, and chemotaxis. KRAS is also an important factor and therapeutic target of cancer because it can mediate glutamine metabolism and regulate the nuclear factor E2-related factor 2, ral guanine nucleotide dissociation stimulator, TENSIN 4, and phosphatidylinositol 3-kinase (PI3K) pathways [KRAS affected lipid accumulation, which is a key step in milk fat synthesis. The expression level of KRAS significantly decreased with adipogenic differentiation, which was associated with changes in KRAS expression during preadipocyte differentiation [KRAS, a member of the RAS family, can affect adipogenesis by regulating mitogen-activated protein kinase (MAPK) in various cell types [KRAS also affects milk fat synthesis in cows.As an oncogene, Kirsten rat sarcoma viral oncogene homolog , unless indicated otherwise.2 atmosphere.MAC-T cells  were cultured with Dulbecco’s Modified Eagle Medium with Ham’s F12 (DMEM/F12) medium  containing 10% fetal bovine serum  at 37 °C in a 5% COIn brief, MAC-T cells were plated in 12-well plates . Then, the culture medium was replaced with DMEM/F12. Lipofectamine 2000 Transfection Reagent /siRNA  complexes were added at 1:2 ratios to the plates. The final concentration of siRNAs was 80 nM in DMEM/F12 medium. After 6 h, the medium was changed to DMEM/F12 with 10% fetal bovine serum, and the culture plates were incubated for 18 h. The siRNA sequences are shown in In brief, MAC-T cells were cultured in 12-well plates. Then, the cells were treated with 50 μM chloroquine  for 24 h when they reached 70% confluence. Next, the autophagy levels were detected by Western blot and immunofluorescence assays.Cell apoptosis was detected with flow cytometry  according to the instructions provided in Annexin V-FITC apoptosis analysis kit . The details of the materials and methods are shown in the −△△CT method with β-ACTIN as the standard. The details of the methods and the primer sequences are shown in the In brief, total RNA of MAC-T cells was extracted by using TRIzol Reagent . Gene expression was quantified by the Mastercycler ep realplex  and 2In brief, MAC-T cells were trypsinized and collected. Then, RIPA buffer  containing 1% PMSF  was used for lysis. The GAPDH loading controls were performed on the same membrane unless indicated otherwise. The details of the methods and antibodies used in the study are shown in the 5 cells were seeded on 12-well plates containing cell culture slides (NEST Biotechnology). Twenty-four hours after transfection, we performed immunofluorescence detection. The details of the methods and antibodies used in the study are shown in the In brief, a total of 1 × 10In brief, intracellular TG was detected using a tissue/cell triacylglycerol assay kit . The details of the materials and methods are shown in the In brief, 24 h after cell transfection, intracellular ROS levels were detected using 10 µM 2′,7′-dichlorofluorescein diacetate  and flow cytometry . The details of the materials and methods are shown in the According to the manufacturer’s requirements, 24 h after cell transfection, a T-AOC assay kit (Solarbio), CAT assay kit (Solarbio), and reduced GSH assay kit (Solarbio) were used to detect the levels of T-AOC, CAT, and GSH, respectively. The absorption value was measured with a microplate reader . The T-AOC and activity of GSH and CAT were calculated based on the absorption value and standard curve.Briefly, mitochondrial morphology was detected using 200 nM MitoTracker Red CMXRos (Invitrogen). The details of the materials and methods are shown in the Briefly, intracellular ATP levels were measured using an enhancing ATP detection kit (Beyotime). The details of the materials and methods are shown in the The whole genome was extracted by using the QIAamp Fast DNA Tissue Kit . mtDNA was detected using qPCR. The details of the materials and methods are shown in the KRAS groups were identified with cutoff values of p-value < 0.05, log2(FC) > 1, and VIP > 1. The details of the materials and methods are shown in the In brief, 24 h after transfection, MAC-T cells were washed with PBS and digested by trypsin for collection. After sample extraction, the samples were injected into chromatography and mass spectrometry systems for detection. Then, the raw data were processed by using Progenesis QI . For the data extracted by the Metabolites Search, metabolites with RSD > 30% were deleted. Significantly differentially expressed metabolites between the si-NC and si-KRAS groups were identified with cutoff values of p-value < 0.05, log2(FC) > 1, and VIP > 1. The details of the materials and methods are shown in the In brief, 24 h after transfection, MAC-T cells were washed with PBS and digested by trypsin for collection. After sample extraction, the samples were injected into chromatography and mass spectrometry systems for detection. Then, the raw data were processed by using LipidSearch . For data extracted by Lipid Search, lipid molecules with RSD > 30% were deleted. Significantly differentially expressed lipids between the si-NC and si-p-Values < 0.05 were considered statistically significant.All experiments were performed three times unless indicated otherwise. Data are represented as the means ± standard deviation. A Bonferroni post hoc test was used to compare the data. All statistical analyses were performed using the SPSS software . KRAS was localized in the nuclei of MAC-T cells (KRAS-specific siRNA (si-KRAS) significantly inhibited the protein expression level of KRAS in MAC-T cells , 43 kinds of GPs, and 4 kinds of glycerides (GLs) were found (KRAS group and si-negative control (NC) groups based on the weighted coefficients of the OPLS-DA model. The results showed that the metabolite with the highest VIP score was MePC , followed by TG , TG , and TG , CD36, FABP3, ACSL1, ACSS2, ACC, FASN, SCD1, GPAM, LPIN1, and 1-acylglycerol-3-phosphate O-acyltransferase 1 (AGPAT1) were also significantly increased , NADH:ubiquinone oxidoreductase core subunit S8 (NDUFS8), succinate dehydrogenase complex iron sulfur subunit B (SDHB), ATP synthase F1 subunit alpha (ATP5F1A), and ubiquinol–cytochrome c reductase binding protein (UQCRB) and the mitochondrial biogenesis-related genes nuclear respiratory factor 1 (NRF1), DNA polymerase gamma catalytic subunit (POLG), mitochondrial transcription factor A (TFAM), and mitochondrial transcription factor B1 (TFB1M) in the KRAS inhibition group were also significantly increased  and autophagy-related 7 (ATG7) were significantly increased after KRAS inhibition  puncta in the cytoplasm were significantly increased in the NC group A, and thncreased B,C. The ncreased B,D were hibition E. In addce assay F. DetailKRAS treatment. As shown in KRAS-treated group exhibited significantly increased phosphorylation levels of ERK1/2, JNK1/2/3, and p38. Detailed data are shown in Western blotting was used to detect the MAPKs  phosphorylation levels in MAC-T cells with or without KRAS affects lipid accumulation. However, the specific roles of KRAS in the lactation process remain unclear.Milk fat is one of the main nutrients in milk. It not only can provide energy for newborn calves but is also an important raw material for many dairy products . Milk faKRAS inhibition leads to increased expression levels of PPARG and SREBP1, suggesting that inhibition of KRAS may affect the localization of key nuclear transcription factors, thus improving the ability to promote lipid droplet formation in MAC-T cells [CD36, FABP4, ACSL1, ACC, FASN, SCD1, and AGPAT6 [KRAS inhibition may increase lipogenic gene expression; the transcriptional activation of these genes is consistent with the abundant milk fat synthesis that occurs at the onset and throughout lactation. Moreover, genes related to FA metabolism, TG synthesis, and lipid droplet formation, such as ACSS2, GPAM, LPIN1, and XDH, were also increased after KRAS inhibition. Once FAs with >10 carbons are formed, they are activated by ACSL1 and bound to FABP3, which allows the FAs to enter the TG synthesis pathway [KRAS may mediate signaling that can activate or deactivate some transcription factors and affect the translocation of PPARG, SREBP1, and FABP4 to regulate TG formation in MAC-T cells. Moreover, previous studies have also shown that IGF-1 promotes adipogenesis [KRAS inhibition significantly increased the protein expression levels of IGF1R and decreased the protein expression levels of TGFβR1 in MAC-T cells, which suggests that KRAS has potential roles in regulating the IGF and TGF signaling pathways. The increased levels of EGF and EGFR expression after KRAS inhibition also indicate that KRAS can regulate the process of intracellular lipid formation and lipid accumulation [In mammalian mammary glands, most luminal epithelial cells express cytokeratin 18 (CK18) . Through-T cells . Increasd AGPAT6  also ind pathway . Short-c pathway . Once TG pathway  and for  pathway . These rogenesis , while bogenesis . Howeverogenesis . TGFβ reogenesis . In thismulation .KRAS inhibition-mediated ER stress-signaling pathway may be associated with lipogenesis. Curiously, although increased milk fat formation often leads to increased ER stress, the protein levels of GRP78 and CHOP were significantly improved, while ATF6 levels were not changed significantly after KRAS inhibition. These results showed that among MAC-T cells, the regulation of KRAS in ER stress may be different from the result that the expression of GRP78 can be activated by ATF6, a target of the ER stress response, which has been implicated in the ER stress response pathway [KRAS inhibition. These results indicated that KRAS-mediated ER stress did not induce cell apoptosis in MAC-T cells. However, this may be affected by the knockdown efficiency of siRNA.The ER is the only place where milk fat synthesis occurs, and its activity changes during lactation ,49. Duri pathway . This ma pathway . Our res pathway , and apoKRAS inhibition, the total capacity to produce milk fat and mitochondrial function were not negatively affected in MAC-T cells. This indicated that after KRAS inhibition, despite the increase in ROS under the intensity metabolic influence, oxidative homeostasis was maintained, as the antioxidant defenses were sufficient. After KRAS inhibition, oxidative homeostasis may be maintained by the significantly increased levels of GSH and CAT.Moreover, the MECs of lactating cows undergo intensive metabolism in cells and accumulate a large amount of free radicals, such as ROS . MECs inKRAS may affect the synthesis of milk fat by regulating the level of mtDNA, intracellular ATP, and the related gene expression of the mitochondrial electron transport chain  and mitochondrial biogenesis  [KRAS inhibition, some MAC-T cells with higher lipid production contained mitochondria with morphological changes. Through electron microscopy, previous studies found that during the early postpartum period, both the size and density of secretory cell mitochondria increase, which also indicated that the increase in milk production during early lactation is linked with a dramatic increase in mammary mitochondrial number [KRAS inhibition lead to the increased level of their own biosynthesis and ATP [KRAS inhibition. These results suggested that after KRAS inhibition, the highly dynamic mitochondrial fusion and fission cycle was proposed to balance the compensation of damage by fusion and the elimination of damage by fission, eventually leading to an increase in mitochondrial biosynthesis.Mitochondria are recognized as essential components for meeting energy needs and maintaining metabolic homeostasis because mtDNA is a template for several polypeptides in respiratory chain complexes that are essential for ATP generation . The decnd POLG) . Interesl number . However and ATP  or wheth and ATP . Therefo and ATP . Studies and ATP . The mit and ATP . This is and ATP . Our resKRAS inhibition, the number of autophagosomes, protein levels of LC3B-II/LC3B-I, and mRNA levels of BECN1 [ATG7 [After of BECN1  and ATG7N1 [ATG7  were sigN1 [ATG7  was signATG7 and BECN 1 will not increase. However, in this study, KRAS inhibition resulted in increased mRNA levels of ATG7 and BECN 1. These results indicated that the inhibition of KRAS indeed increases the level of autophagy rather than preventing the degradation of autophagosomes and inhibiting autophagy in MAC-T cells. In addition, the colocalization level of autophagosomes with ER and lipid droplets was enhanced after KRAS inhibition, which indicates that the level of selective autophagy, that is, ER-phagy and lipophagy, was enhanced. ER-phagy and lipophagy represent the major quality control mechanisms that ensure that exhausted or harmful ERs are properly renewed and that the balance of lipid metabolism is maintained [KRAS inhibition, TG levels were significantly increased, and lipid metabolism was disordered, so lipophagy may be initiated to maintain lipid homeostasis. When excessive lipid synthesis and secretion occurs, the ER suffers from stress and damage, and the level of intracellular ROS also increases significantly, which may together lead to an increased level of ER-phagy. Many studies have shown that the activation or inhibition of mTOR can upregulate or downregulate milk fat levels, respectively [KRAS inhibition was the opposite of what we observed in our research. This may be because after KRAS inhibition, the increased level of stress caused by excessive TG accumulation inhibits mTOR activation, which enhances autophagy levels to maintain the homeostasis of intracellular environments [Chloroquine inhibits the degradation of autophagosomes, thereby inhibiting autophagy levels in cells. During this process, the mRNA expression levels of intained . This inectively ,71. Interonments ,72.KRAS inhibition decreased the levels of ERK activation and PPARG expression, which suggests that after KRAS inhibition, the levels of FAS expression and lipid accumulation were increased by enhancing the expression of C/EBPα/β and PPARG that was caused by ERK inhibition [KRAS inhibition also decreased the phosphorylation level of p38, indicating that KRAS has the potential to regulate the activation of the nuclear factor of activated T cells 4 (NFATc4) and enhance the nuclear translocation of NFATc4 after dephosphorylation by NFAT, thus promoting the expression of PPARc and lipid formation [KRAS inhibition can activate the JNK pathway, thus promoting lipid formation by regulating PPARG and FABP4 [KRAS inhibition, the phosphorylation level of JNK increased, while the levels of ERK and p38 decreased at the same time, which suggests that KRAS plays important roles in lipid accumulation in MAC-T cells.ERK, p38, and JNK MAPKs are intracellular signaling pathways that play a pivotal role in adipogenesis . In thishibition . Convershibition . Some sthibition . In thisormation . The inhormation ; these rnd FABP4 . After KKRAS inhibition, the intracellular metabolite levels changed significantly; most of these metabolites were lipid metabolites, and the top 30 metabolites with VIP scores also contained 28 lipid metabolites. As the main component of milk fat, TG also regulates cellular homeostasis and the levels of other lipid metabolites [KRAS inhibition, various forms of TG were significantly increased, and one form of PI was significantly decreased. Moreover, the levels of SM, Hex1Cer, and Hex2Cer were negatively correlated with the Cer and SPH levels after KRAS inhibition. The Cer formed from SM turnover might be hydrolyzed by ceramidases to SPH [KRAS inhibition, various forms of SP and GP were differentially expressed, and VIP score analysis also showed that TG was the main contributor to the difference between groups. These results indicated that KRAS inhibition has important effects on both the milk fat globule core and MFGM and that the cellular stress caused by excessive TG accumulation promotes the transformation of Hex1Cer, Hex2Cer, and SM into Cer and SPH. This result also seems to indicate that KRAS-mediated TG level changes in the milk fat globule core may lead to the different MFGM components.The metabolomics analysis showed that after abolites . Previouabolites . This iss to SPH ,83. Cells to SPH . SP not s to SPH . After KKRAS inhibition. The phosphorylated levels of MAPKs  were also changed after KRAS inhibition (KRAS inhibition, the levels of some lipids were also regulated in addition to TG, such as PC, PE, Cer, and HexCer. These findings broaden the understanding of the effects of KRAS on milk fat metabolism and provide a new theoretical basis for further exploring the mechanism of milk fat synthesis and secretion. With KRAS inhibition, the mitochondrial function and milk fat synthesis capacity of MAC-T cells were significantly enhanced, while intracellular ROS and ER stress levels were increased. Thus, although KRAS inhibition enhanced milk fat secretion, it simultaneously stressed mammary epithelial cells and enhanced autophagy levels, which is not good for the health of the cows. Conversely, relatively high KRAS expression may lead to a decrease in milk fat secretion, which is detrimental to offspring feeding. Therefore, although KRAS is a key regulator of milk fat secretion, simply changing its expression level up- or downregulation may be detrimental to the health of either the next generation or dairy cows. This is similar to the occurrence of ketosis in mammary tissue, which suggests that abnormal expression of KRAS may cause a series of symptoms by affecting energy imbalance and play important roles in ketosis. Therefore, the regulation of KRAS may also provide new directions for exploring the specific mechanism and treatment of ketosis.Overall, the levels of TG, mitochondrial functions, ROS, ERS, and autophagy were increased in MAC-T cells with hibition . MoreoveKRAS regulated the phosphorylation levels of ERK, p38, and JNK. After KRAS inhibition, mitochondrial functions were improved. However, ROS and ERS were also increased. This would also lead to an increase in intracellular autophagy to maintain oxidative homeostasis and remove damaged ER. Excessive TG accumulation caused by KRAS inhibition led to intensive lipid metabolism and secretion, which suggests that KRAS plays important roles in lipid accumulation.In MAC-T cells,