Different starch botanical sources contain different starch architectures, which may alter sweetener-starch interactions and the effects of sweeteners on Tgels. To document these effects, the Tgels of wheat, potato, waxy corn, dent corn, and 50% and 70% high amylose corn starches were determined in the presence of eleven different sweeteners and varying sweetener concentrations. Tgels of 2:1 sweetener solution:starch slurries were measured using differential scanning calorimetry. The extent of Tgel elevation was affected by both starch and sweetener type. Tgels of wheat and dent corn starches increased the most, while Tgels of high amylose corn starches were the least affected. Fructose increased Tgels the least, and isomalt and isomaltulose increased Tgels the most. Overall, starch Tgels increased more with increasing sweetener concentration, molar volume, molecular weight, and number of equatorial and exocyclic hydroxyl groups. Starches containing more short amylopectin chains, fewer amylopectin chains that span through multiple clusters, higher number of building blocks per cluster, and shorter inter-block chain lengths exhibited the largest Tgel increases in sweetener solutions, attributed to less stable crystalline regions.The gelatinization temperature (T Starch granules are botanical energy storage vessels that differ in size 2–100 μm) and molecular architecture between botanical sources, although all native starches share the traits of being semicrystalline, water insoluble, and composed predominantly of two α-glucans: amylopectin and amylose 00 μm and. Amylosegels of starches from different botanical sources vary [gels of starches include: the crystalline form of starch [gels of starches: starches with higher amounts of short amylopectin chains (DP 6–12) have lower Tgels, and starches with higher percentages of longer amylopectin chains (DP 14–25) have higher Tgels [gels because these conformations favor more hydrogen bonding between double helices and resist plasticization during heating [gels.Despite the commonality that starch gelatinization is the melting of amylopectin crystals, the Tces vary . Structuf starch , amount f starch , amylopef starch ,14,15, af starch ,17. Braner Tgels ,19,20. A heating . Therefogel. Early theories of why sugars increase the Tgel were: sugars decrease the water activity (aw) and moisture content [g) of the amorphous fraction [gel increases were correlated to sweetener solution viscosity and the number of exocyclic and equatorial hydroxyl groups on the sweetener, and that Tgel increases were not strongly correlated to aw and dry sweetener Tgs. In a follow-up study by van der Sman and Mauer [gel increase for wheat starch in sweetener solutions was presumed to be due to sweetener-starch intermolecular hydrogen bond interactions, with a greater number and strength of these interactions resulting in increased Tgels.The presence of solutes, such as sugars, also affects the T content ,22,23; sfraction ,25; and fraction ,27,28. Tfraction , who found Mauer , sweetengel have largely been studied within the scope of a single starch type and without regard to the potential effects of starch granule differences between starch botanical sources and compositions. To expand on our earlier study of the effects of 19 sweeteners on wheat starch Tgel [gels of six starches with varying granule morphologies  to elucidate the effects of starch botanical sources (and hence structure) on sweetener-starch interactions and resultant Tgel elevation.The effects of sugars on Trch Tgel , the obj® dent corn starch (dent corn), Amioca waxy corn starch (waxy corn), PenPure® 10 potato starch (potato), Hylon® VII high amylose (≈70% amylose) corn starch (HACS70), and Hylon® V high amylose (≈55% amylose) corn starch (HACS55) were donated by Ingredion Inc. , and Aytex® P wheat starch was donated by ADM   . All staMN, USA) . Sodium The 6-carbon (6-C) sweeteners studied consisted of aldoses , a ketose (fructose), and an alditol (sorbitol). Solutions were prepared at 1, 2, 3, and 4 molar (M) concentrations for the 6-C sweeteners except for galactose, for which only 1 and 2 M solutions were used due to solubility limitations. The 12-carbon (12-C) sweeteners consisted of reducing sugars , non-reducing sugars , and sugar alcohols , and solutions were prepared at 0.5, 1.0, and 1.5 M concentrations, with an additional 2.0 M sucrose solution. The sweetener solutions were prepared in 10 mL volumes in 15 mL centrifuge tubes. Water was added to the sweetener to ~80% of the final volume, then the tube was placed in a heating block at 80 °C for several minutes followed by vortexing on a VWR Vortex Mixer  and/or slower rotational mixing using a Scientific Industries Roto-Shake Genie  until the sweetener fully dissolved. Upon cooling to near ambient temperature, water was added to reach a final 10 mL volume. Solutions were not used if crystals were visible after overnight storage at ambient conditions.gels) of the starch-sweetener slurries were measured using a method adapted from Allan, Rajwa and Mauer [w/w starch slurries were made by combining 1 part starch with precisely 2 parts solution in 1.5 mL centrifuge tubes, mixing with a stainless steel pin, and vortexing until the slurry appeared to be a homogenous mixture (no visible dry starch). The samples were then capped and stored overnight at ambient temperature (~22 °C). Before analysis, samples were vortexed to re-suspend the starch, and 15 to 20 mg of slurry were transferred into a Perkin Elmer 50 µL pan (BO143017) and hermetically sealed with a lid (BO143003). Due to analysis temperatures exceeding 115 °C and risk of pan failure, the HACS70 and HACS55 samples were loaded into high-pressure Perkin Elmer 50 µL pans (B016-9321) and hermetically sealed with high-pressure lids (B016-9321). Pans were manually transferred into a Perkin Elmer DSC 4000  that was calibrated using water, indium, and zinc. The potato, dent corn, and waxy corn samples were heated from 30 °C up to 100–115 °C at 10 °C/min, while HACS70 and HACS55 samples were heated from 30 °C up to 115–130 °C at 10 °C/min. The Tgel of each slurry was measured in triplicate. The Tgel was identified as the endothermic event occurring around 60 to 103 °C, and the onset temperature, peak temperature, and enthalpy (∆H) of starch gelatinization were determined using the “peak calculation” function with the “Standard” baseline in Pyris Software (version 10.1.0.0412). Tgel peak analysis was performed using data that encompassed ~5 to 10 °C before the peak onset to the post peak heat flow maxima (~2 to 5 °C after the peak end) . The onset Tgel of HACS70 was calculated using the “onset” function due to lack of a clear end of peak and/or only partial gelatinization occurring within the experimental parameters. This onset was determined by the intersection of two tangents, where the 1st tangent was the slope before gelatinization and the 2nd tangent was the slope between the onset and peak. Examples of DSC analysis for starch in water are shown in gels were compared further in this study.The gelatinization temperatures (Tnd Mauer . Approxieak end)  while enthe peak . The ∆H gel of potato starch in 1 M glucose solutions were determined using a modified sample preparation method prior to DSC analysis. An initial 150 g sample of a 1:2 w/w starch slurry made with 1 M glucose was mixed in a 250 mL beaker, and then the pH was adjusted to 4, 5, 6, 7, 8, 9, and 10 using 1 M glucose solutions that had been pH-adjusted using 1 M NaOH or 1 M HCl. The pH was measured using an Orion pH probe  calibrated from pH 4 to 10. The pH of the slurry was adjusted rather than the glucose solution to avoid any unaccounted pH buffering from the phosphates in the potato starch. The final starch-to-slurry w/w ratio was no longer precisely 1:2 w/w due to the addition of the acidified and alkalinized 1 M glucose solutions, but this was not a major concern since the Tgel onset in an abundance of solution is not affected by minor starch:solution ratio changes [gel of each pH-controlled slurry was then measured in triplicate.The effects of pH on the T changes . The slugel of a starch were compared by grouping the Tgels of a starch in sweetener solutions with equal monosaccharide unit concentrations , followed by one-way ANOVA and Tukey post-hoc tests (α = 0.05). The effects of a sweetener solution on the Tgels of different starches were investigated by comparing ΔTi-0)gel(s. The ΔTi-0)gelgel(s between starches in a sweetener solution were identified using one-way ANOVA and Tukey post-hoc tests (α = 0.05). The Tgels of potato starch in 1 M glucose solutions at pHs 4–10 were also compared using one-way ANOVA and Tukey post-hoc tests (α = 0.05). The associations of categorical sweetener solution properties to effects on Tgels were investigated using four-way ANOVA with all possible two-way interactions (α = 0.05). The categorical sweetener solution properties were: monosaccharide unit concentration , sweetener size (6-C or 12-C), sweetener type , and if the sweetener was a reducing sugar. The correlation of quantitative starch , average amylopectin chain length, percentage of DP 6–12, percentage of DP 13–24, percentage of DP 25–36, percentage of DP ≥ 37, number of building blocks per cluster (NBbl), and inter-block chain lengths (IB-CL). The quantitative sweetener solution properties were: number of hydroxyl groups for intermolecular H-bonding , number of equatorial and exocyclic hydroxyl groups, dry glass transition (Tg), sweetener molar volume, and capacity factor (cK) (i-0)gel(s in sweetener solutions at 3 M monosaccharide unit concentrations (ΔTgel(3M-0)) and with the slope of the log10 of Tgels in respect to molar monosaccharide unit concentrations (log Tgels). The significance of the Pearson correlation coefficient (R) was assessed using t-statistics:n−2 degrees of freedom. The significance of crystalline starch type (A or B-type) was evaluated using a two-tailed t-test. The effective water contents  of sweetener solutions were extracted from van der Sman and Mauer [t-statistics were calculated using Microsoft Excel 365 .The effects of sweeteners on the Te starch  and sweee starch  to the Ttor (Kc) . These snd Mauer . All ANOgel of wheat starch, and increasing sweetener concentrations resulted in further increases in Tgel ) and the slope of the log of Tgels with respect to the sweetener molar monosaccharide unit concentration (log Tgel). Significant correlations (p < 0.10) were found between Tgel increases in sweetener solutions and both the number of equatorial and exocyclic hydroxyl groups on the sweetener and the sweetener molar volume, whereas the correlations of Tgel with NOH,s, Tg, and Kc were not significant  (TableS 2 and 4). In Allan, Rajwa and Mauer [g on the Tgel was doubted due to the presence of extreme outliers and evidence the Tg of native starch is well below the Tgel [OH,s was derived from the Tg of the sweetener [gel with either Tg or NOH,s. However, the effective water content , which is directly related to volumetric density of intermolecular hydrogen bonds of the sweetener solution and calculated using NOH,s [gel gel in the presence of sweeteners compared to the other corn starches. Exceptions were the ΔTi-0)gelgel was significant for the Tgels of all corn starches but not for wheat starch Tgels  equal to or greater than in the counterpart sugar solutions   . Baek, Ygel elevation of corn starches were the numbers of equatorial and exocyclic hydroxyl groups and molar volumes (p < 0.10), except not the molar volume with log Tgels of HACS55 gel( of potato starch more than ΔTi-0)gelgel( of potato starch as much as the ΔTi-0)gel, the correlation with the number of equatorial and exocyclic hydroxyl groups increased from R = 0.024 to R = 0.996 (p < 0.05), and the correlation with the molar volume increased from R = 0.340 to R = 0.945 (p < 0.10). The log Tgel of potato starch in galactose solutions was excluded from these correlations due to low galactose solubility resulting in a limited number of data points (only 1 and 2 M). The significant correlations of potato starch log Tgel with 6-C sweetener molar volumes and the number of equatorial and exocyclic OHs suggest the mechanism by which 6-C sweeteners increased the Tgel of potato, wheat, and corn starches was similar. None of the 12-C sweetener properties were correlated with the log Tgels of potato starch, attributed to a potential size limiting effect that altered the sweetener-starch interactions of 12-C sweeteners in the potato starch granule (discussed in detail later).Unlike for other starch types, none of the numerical sweetener factors were significantly correlated to the potato starch Tgel ; howevergels of starches from different botanical sources (i-0)gelgelgel(s were greater than the waxy corn ΔTi-0)gelgel between the ΔTgel(3M-0) or log Tgels of a sweetener and the amylose (apparent and absolute) contents of any of the starches gel had Tgels that were less affected by sweeteners because there were fewer crystal defects. The negative linear correlations between Tgel increases in sweetener solutions and average amylopectin chain lengths, percentages of DP 25-–36, and percentages of DP ≥ 37 , B1 (fb1), B2 (fb2), and B3 (fb3) chains and span through 1, 1, 2, and 3 crystalline amylopectin clusters, respectively [gel because there are fewer possibilities for stabilizing sweetener-starch intermolecular interactions. There were also no significant correlations between Tgel increases in sweetener solutions and percentages of B1 (DP 13-–24) chains, which could be due to the intermediate length between stabilizing and destabilizing amylopectin chain lengths.The architecture of amylopectin has been associated with the thermal properties of starch, attributed to the stability of the starch crystallites ,58,59,60 defects ,19,20. T DP ≥ 37  suggest ectively ,62. The gel, with lower NBbl and higher IB-CL values associated with higher Tgels [gels  tended nd IB-CL . It was nd IB-CL , and stagel. The Tgels of starches that had less stable crystallites, due to shorter amylopectin chain lengths and sterically hindered double helical clusters, were more affected in sweetener solutions than starches with more stable crystalline regions. Since sweetener-starch interactions drive the Tgel increase, starches that have more regions to be stabilized s in A and B-type starches. However, the ΔTi-0)gel(s of dent corn (A-type), wheat (A-type), and potato in 6-C solutions (B-type) starches were the most affected by sweeteners, while the ΔTi-0)gel(s of waxy (A-type), potato (B-type) in 12-C solutions, and high amylose corn starches (B-type) were the least affected (i-0)gelgel increase the Tgel of starch more than 6-C sweeteners (monosaccharides) for other starch botanical sources [gel elevation behavior of potato starch were different than for other starches.There were similar sweetener effects on ΔT sources  with fewner size , since p sources ,29,50. T(1)Size Exclusion within the Granulegel of potato starch compared to elevating the Tgels of starches from other botanical sources was due to some sort of size exclusion, wherein the diffusion of 12-C sweeteners throughout the potato starch granule was affected differently than diffusion in other starch types. The molecular size exclusion could be due to: (1) the spacing of amylopectin branch points, and/or (2) the spacing between blocklets in the crystalline growth rings. In potato starch, the amylopectin branch length from the backbone to the crystalline double helix is ~3 glucosyl units long, much shorter than for waxy rice, tapioca, and wheat starches that have branch lengths of 6, 7, and 7 glucosyl units long, respectively [A speculation as to why 12-C sweeteners were not as effective at raising the Tectively . This shectively . Anotherectively . This lo(2)Phosphorous Contentgel compared to other starches with similar amylopectin structures, because the crystallites are destabilized when the phosphate groups repel from each other during heating [gel. It is important to note that the positive correlation of phosphate content and Tgel could have been confounded with the effects of longer amylopectin chains [gel. Since phosphates are ionizable groups, it was assumed the pKas of potato starch were similar to the pKas of phosphoric acid: there would be no pKa3 at 12.67 due to the ester linkage with starch, pKa1 at 2.21, and pKa2 at 7.21 . A simiom Weast , but the2 ≈ 6.11 ,71. Regat ~65 °C . The lacyl units . Anotheryl units , and DP ts [gels . The Tgeo starch , was also starch .gels exhibited different trends in the presence of 12-C sweeteners compared to Tgels of starches from other botanical sources is that the sweetener diffusion throughout the amorphous regions of the potato starch granule was restricted more than in other starch types, thereby limiting the amount of 12-C sweetener-starch interactions in native potato starch.Therefore, the most likely reason potato starch Tgels of six different starches were investigated. The Tgels of all starches increased in sweetener solutions compared to Tgels in water, with greater Tgel increases as sweetener concentration increased; however, the magnitude of the increase in Tgel varied between different types of sweeteners and starches. The starch Tgel was increased more by sweeteners that had larger molar volumes and higher numbers of equatorial and exocyclic hydroxyl groups . Sweeteners with these properties formed more sweetener-starch interactions, stabilizing the amorphous starch regions, and thereby increased the Tgel to a greater extent. Sweetener traits that were not associated with increases in starch Tgel were: dry Tg, cK, NOH,s, and whether or not the sweetener was a reducing sugar. The amylopectin structure of starches also affected the Tgel increase in sweetener solutions. The Tgels of starches with higher percentages of A-chains (DP 6–12) increased the most in sweetener solutions, whereas the Tgels of starches with higher percentages of B2 (DP 25-–36) and B3 (DP ≥37) amylopectin chains increased less in sweetener solutions. The amylopectin fine structure also influenced the effects of sweetener solutions on the Tgel, where starches with fewer building blocks per cluster and longer inter-block chain lengths formed more stable crystals and the Tgels were less affected. A-type starches were more affected by sweeteners than B-type starches, but this may be confounded with B-type starches having longer amylopectin chains. Starch traits that were not associated with Tgel increases in the presence of sweeteners included: amylose content and percent crystallinity. The effects of sweeteners on the Tgel elevation of potato starch were unique, because the potato starch Tgel was one of the most affected by 6-C sweeteners and the least affected by 12-C sweeteners. This was attributed to a size exclusion phenomenon that altered 12-C sweetener diffusion into the potato starch granules and was not due to ionic effects of the phosphate monoesters. In summary, starches with less stable crystalline regions are more susceptible to sweetener-starch stabilizing effects and exhibited greater increases in Tgel in the presence of sweeteners. Native starches with structures that limit sweetener-starch interactions, such as the more stable crystalline regions in high-amylose corn starches or the unique structures in potato starch that limit diffusion of 12-C sweeteners, exhibit smaller increases in Tgel. However, sweeteners with molecular conformations that were favorable for intermolecular interactions increased the Tgel the most, regardless of starch architecture. This study has shown the different Tgel elevation effects of sweeteners on multiple starch botanical sources and provided insights into structural reasons for these differences, information that is useful for improving the understanding of structure-function relationships and behaviors of starches in formulations containing different sweeteners.The effects of 6-C and 12-C sweeteners on the T


Cardio‐oncology is a clinical discipline focused primarily on the early detection of anticancer therapy–related cardiomyopathy. However, there is growing evidence that the direct adverse consequences extend beyond the myocardium to affect the vasculature, but this evidence remains limited. In addition, there remains a paucity of clinically based strategies for monitoring vascular toxicity in these patients. Importantly, arterial stiffness is increasingly recognized as a surrogate end point for cardiovascular disease and may be an important vascular outcome to consider. Therefore, the aim of this systematic review and meta‐analysis was to summarize evidence of increased arterial stiffening with anticancer therapy and evaluate the effect of treatment modifiers.CI, 0.448–1.332; P<0.0001; mean difference, 1.505; 95% CI, 0.789–2.221; P≤0.0001) and in patient versus control groups . Subgroup analysis indicated differences in arterial stiffness between anthracycline‐based and non‐anthracycline‐based therapies , but not follow‐up time.A total of 19 longitudinal and cross‐sectional studies that evaluated arterial stiffness both during and following anticancer therapy were identified using multiple databases. Two separate analyses were performed: baseline to follow‐up (12 studies) and control versus patient groups (10 studies). Subgroup analysis evaluated whether stiffness differed as a function of treatment type and follow‐up time. Standard mean differences and mean differences were calculated using random effect models. Significant increases in arterial stiffness were identified from baseline to follow‐up (standard mean difference, 0.890; 95% Significant arterial stiffening occurs following anticancer therapy. Our findings support the use of arterial stiffness as part of a targeted vascular imaging strategy for the identification of early cardiovascular injury during treatment and for the detection of long‐term cardiovascular injury into survivorship. Aodaortic distensibilityCVDcardiovascular diseaseMDmean differencePWVpulse wave velocitySMDstandard mean differenceExposure to anticancer therapy is associated with increased arterial stiffness in patients with cancer during treatment duration and continues years into survivorship.Patients with cancer with prior anticancer therapy exposure were found to have a 1.4 and 1.5 m/s increase in pulse wave velocity, a measure of arterial stiffness, on average when compared with healthy controls and pretreatment values, respectively.Currently, there are no guidelines for monitoring vascular health in patients with cancer through the progression of treatment and into survivorship.Our findings support the use of noninvasive imaging strategies, such as pulse wave velocity, to monitor changes in vascular health and to assess the risk for development of cardiotoxicity with exposure to cancer treatments.Furthermore, these findings support the paradigm that there are global cardiovascular consequences that extend beyond the myocardium and the cardiovascular system should be considered as a whole when caring for a patient with cancer.Anticancer treatments, including anthracyclines, alkylating agents, and vascular endothelial growth factor inhibitors, are associated with direct vascular damageAn increasingly recognized surrogate end point for cardiovascular disease (CVD) is local and regional measurements of arterial stiffness. In noncancer populations, arterial stiffness is independently predictive of all‐cause mortality and fatal/nonfatal cardiovascular outcomes and is used in CVD risk stratification.The authors declare that all supporting data are available within the article and its online supplementary files.Studies evaluating the relationship between arterial stiffness and anticancer therapy were retrieved from a systematic review of English literature in the Cochrane, PubMed, Google Scholar, and Web of Science databases until January 2019 by members of the research team  with assistance from university research data informationists. The population search terms included “cancer,” “chemotherapy,” and “cardiotoxicity”; the descriptor search terms were “arterial stiffness,” “pulse wave velocity,” and “augmentation index.” Data sources were also identified through manual searches of references in the articles. All search results were downloaded to a research management system  where data extraction began by removing duplicates, review articles, and letters to the editor. All remaining results underwent a full‐text review to determine eligibility in the analysis. The literature search and selection of studies was done by 2 independent reviewers , and disagreements were resolved by consensus. This analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses and registered at the International Prospective Register of Systematic Reviews .Studies were considered eligible if they met the following criteria: (1) full‐length publication in a peer‐reviewed journal, (2) evaluated arterial stiffness via pulse wave velocity and or aortic/carotid stiffness, and (3) reported anticancer drugs used to treat cancer that have previously been associated with long‐term CVD risk.We included 3 separate modalities in our analyses that evaluated arterial stiffness both as a local measurement of specific vessels via aortic distensibility (AoD) and β‐stiffness index (carotid artery and aorta) and regional assessments of pulse wave velocity (PWV) that gives an index of global arterial health . In our literature search, PWV, AoD, and β‐stiffness index were the most commonly reported and more important are validated measurements for assessing arterial stiffness.Data were extracted by 2 authors  according to the Preferred Reporting Items for Systematic Review and Meta‐analyses statement2 statistic. After examining the Cochran Q test and I2 statistic, significant heterogeneity was revealed, so we proceeded with a random effects model to minimize bias.P<0.05.All statistical analyses were performed using the software R (version 3.5.1) with package meta. The effect sizes were calculated using the inverse variance method. Heterogeneity was evaluated using the Cochran Q test and Higgin IOur search identified 338 publications, which was narrowed by preliminary review to 189 after removing duplicates Figure . Article30 demonstrated a 2‐fold increase in carotid–femoral PWV in just 4 months from baseline measurement and a 3‐fold increase compared with the control group. Similarly, Drafts et al15 reported a rapid increase in arterial stiffness in the first month of the monitoring period with a 51% increase during the course of the full 6 months after correcting for baseline blood pressure. Importantly, these ranges of arterial stiffness increase are similar to that reported in aging populations and those with atherosclerosis,1 found no differences in carotid–femoral PWV from baseline to 6 months following the last anthracycline treatment. However, half of the participants in that study were on cardiovascular‐related medications  or supplemented with tamoxifen (58% of patients), all of which are known to have positive vascular effects that could have neutralized detrimental vascular effects from anthracycline administration.Of the included studies, 14 assessed arterial stiffness in patients who received primary anthracycline chemotherapy.48 demonstrated in a preclinical model that an increase in aortic matrix metalloproteases within days of doxorubicin administration, thus highlighting the potential for significant vascular remodeling during and in the months following treatment.It is important to note the high variability in magnitude of the acute increase in arterial stiffness between the identified studies. Similar ages of patients and treatment paradigms suggest that additional factors may contribute to the magnitude of stiffening that occurs with anthracycline chemotherapy. Although unknown that this time, these factors may include a patient's baseline cardiovascular health, use of combination therapy, simultaneous treatment with cardiovascular medications, and measurement modality. Additional future work is needed to elucidate what underlying factors increase the risk for large increases in arterial stiffness with anthracycline chemotherapy. Physiologically, these acute changes could be attributed to numerous factors including endothelial dysfunction, altered smooth muscle tone, and changes in the extracellular matrix regulated by factors such as catabolic matrix metalloproteases.34 reported a 13% increase in aortic PWV in children and adolescents  treated with anthracycline chemotherapy compared with healthy sex‐matched and age‐matched controls with an average follow‐up time of 2 years following chemotherapy administration. Conversely, Krystal et al35 reported no differences in carotid–femoral PWV between 51 age‐matched and sex‐matched controls and 68 adolescent childhood cancer survivors with an average follow‐up time of 7 years from end of treatment. However, in a subgroup analysis, patients >18 years old had a 10% increase in carotid–femoral PWV compared with >18‐year‐old controls, suggesting that older childhood cancer survivors develop chronic changes in arterial stiffness 5 to 10 years following treatment. Finally, and most notably, Jenei et al33 reported a 3‐fold increase in β stiffness with a 10‐year follow‐up period in adolescent childhood cancer survivors when compared with age‐matched and sex‐matched controls, further indicating that alterations in vascular integrity persist years to decades following anthracycline chemotherapy. Use of radiation therapy is commonly prescribed in the treatment of breast cancer and has been shown to increase arterial stiffness in cancer survivors.In addition to acute changes in arterial stiffness, most long‐term effects of arterial health have been reported in childhood cancer survivors. Our search included 4 studies in adults, adolescents, and children who were treated with anthracycline chemotherapy as young children, and all 4 reported significant increases in arterial stiffness when compared with age‐matched controls with a follow‐up time ranging from 1 to 20 years. Herceg‐Cavrak et al14 reported an acute increase in PWV of 11% in the first 7 to 10 days of therapy administration and statistically significant increases up through the second visit; however, further significance was not observed in either the third or fourth visit. The authors suggested the lack of continued significance may be attributed to the development of hypertension (systolic blood pressure >140 and/or diastolic blood pressure >90) in 49% of patients treated with anti‐angiogenic drugs; thus, 30% of patients were prescribed calcium channel blockers to control blood pressure. Regardless, other cardiovascular measures such as carotid stiffness assessed by ultrasound remained significant throughout the study with an overall increase of 13% from baseline to the fourth visit. Similarly, patients receiving anti‐angiogenic tyrosine kinase inhibitors had a 9% increase in PWV 6 weeks into treatment.One study included in our analysis assessed arterial stiffness in patients receiving vascular endothelial growth factor tyrosine kinase inhibitors.29 reported no change in aortic PWV with measurements at baseline, 3 months, and 9 months follow‐up that could be attributed to a small sample size (n=19). In contrast, Sekijima et al32 reported a 10% increase in brachial–ankle PWV from baseline to 12 months posttreatment as well as changes in other cardiovascular measures. This suggests that arterial stiffness persists chronically and is prevalent up to a year posttreatment with alkylating agents.Two studies examined the association of alkylating agents and arterial stiffness.Two separate meta‐analyses were conducted to determine if increases in arterial stiffness were associated with exposure to anticancer therapy. We separately examined the association over time in patients from pretreatment baseline to follow‐up after treatment and between controls and patient groups with anticancer therapy exposure.P≤0.001) Q statistic  and I²  in both SMD and MD analyses, respectively. The random effects meta‐analysis revealed a significant increase in arterial stiffness after anticancer therapy in patients with cancer. This increase was seen in the months following chemotherapy compared with baseline values before the start of treatment  Q statistic  and I²  for the SMD and MD analyses, respectively. The random effects meta‐analysis revealed that arterial stiffness was significantly greater in cancer survivors treated with anticancer therapy than in healthy controls . Forest plots for the time point and drug comparisons against control groups can be found in Figure Table To ensure reliability of the present meta‐analyses with our high scores of heterogeneity, we performed a sensitivity analysis to evaluate the robustness of our SMD and MD. The one‐by‐one removal of studies revealed significance in a random effect model that was maintained through the entire analysis. Sensitivity analysis showed that the SMD and MD did not vary substantially with the exclusion of any one study.The present systematic review and meta‐analysis represent the most recent and updated work summarizing the evidence for increases in arterial stiffness in patients with cancer receiving anticancer therapy, which has previously been hypothesized as one of several major contributing factors for the increased risk of premature CVD in this population.The present study showed that anticancer therapy is associated with an increase in arterial stiffness, supporting the concept that anticancer therapy–induced cardiotoxicity extends beyond the left ventricle with direct vascular damage.In our literature search, we came across various methods of measuring arterial stiffness, including both local and regional measurements of arterial stiffness and measures of compliance, distensibility, and elasticity. In the present study, we included measures of regional stiffness (PWV) and local measurements , all of which have been shown to be associated with the manifestation of CVD.Exact pathophysiological mechanisms for increased arterial stiffness following anticancer chemotherapy are currently not known; however, we speculate that many of the same mechanisms contributing to arterial stiffness in response to aging and various types of CVD14 adjusted for changes in pressure, and Res et al27 reported no changes in pressure. Although this does not exclude the possible confounding effects of small changes in pressure on the changes in stiffness observed, it does suggest that other factors may be at play. Future prospective investigations are needed to further evaluate the relationship between changes in arterial stiffness as it relates pressure in those treated for cancer and their impact on clinical outcomes.It is also well established that arterial blood pressure can significantly impact measurements of arterial stiffness and must therefore be considered with interpretation of the changes in arterial stiffness reported in the present analysis.Increased arterial stiffness is relevant for patient prognosis as greater arterial stiffness is associated with all‐cause mortality and fatal/nonfatal cardiovascular outcomes  and is thus increasingly used in CVD risk stratification models.Previous work has investigated potential therapeutic interventions to restore arterial elasticity in ageing populations and decrease stiffness in patient populations. Increased carotid artery distensibility and decreased β stiffness has been reported in middle/older aged men and women following moderate‐intensity and high‐intensity aerobic exercise interventionsThere were 4 main limitations of the present meta‐analysis and systematic review. First, as discussed previously, potential methodological limitations include dependence on blood pressure and age. Only 4 of the included studiesThe results of the present meta‐analysis show an associated increase in arterial stiffness in patients receiving anticancer therapy when compared with healthy age‐matched and sex‐matched controls and from baseline before treatment when compared over time during treatment or after completion of treatment. Local and regional arterial stiffness measurements have independent predictive ability in all‐cause mortality and cardiovascular events in various patient populations who share similar cardiovascular risk factors as patients with cancer receiving cardiotoxic anticancer therapy. These findings support the need to measure vascular health outside of monitoring changes in left ventricular function in this population through the course of treatment to monitor/prevent the onset of overt CVD.None.None.Figures S1–S3 References 1, 8, 10, 13, 16, 19, 20, 22, 26–29, 39, 41, 53, 55, 60, 72, and 76Click here for additional data file.


N-alkylation. A convenient method for pre-azidation of starch was developed. The structures of the derivatives were analyzed using FTIR and 1H NMR. The radicals scavenging abilities of the derivatives against hydroxyl radicals, DPPH radicals, and superoxide radicals were tested in vitro in order to evaluate their antioxidant activity. Results revealed that all the cationic starch derivatives (2a–2e), as well as the precursor starch derivatives (1a–1e), had significantly improved antioxidant activity compared to native starch. In particular, the scavenging ability of the derivatives against superoxide radicals was extremely strong. The improved antioxidant activity benefited from the enhanced solubility and the added positive charges. The biocompatibility of the cationic derivatives was confirmed by the low hemolytic rate (<2%). The obtained derivatives in this study have great potential as antioxidant materials that can be applied in the fields of food and biomedicine.In this study, starch was chemically modified to improve its antioxidant activity. Five novel cationic 1,2,3-triazole functionalized starch derivatives were synthesized by using “click” reaction and  Natural polysaccharides with advantages of renewability, biocompatibility, and biodegradability are considered as potential substitutes for traditional fossil resources, which have been used to prepare a variety of functional materials, covering different fields such as biomedicine , pharmacd-(1-4) and/or α-d-(1-6) glycoside bonds [Starch is the main dietary source of carbohydrates for human beings and the most abundant plant-derived storage polysaccharide. As a kind of natural polymer, starch is formed by condensation of glucose units through α-or α-d-1- glycosidde bonds . Drug dede bonds , implantde bonds , and actde bonds  are the Recently, more attention has been focused on free radicals and antioxidants. It is generally accepted that oxidative stress is closely related to many diseases, such as chronic prostatitis, chronic pelvic pain syndrome, male infertility, cardiovascular diseases, Alzheimer’s disease, and cancer . OxidatiThe anhydroglucose unit (AGU) of starch provides three active hydroxyl groups, including one primary and two secondary hydroxyl groups, making it convenient for chemical modification of starch. Many efforts oriented towards different application purposes have been focused on the chemical modification of starch. Chemical modifications of starch mainly involve acid hydrolysis, cross-linking, acetylation/esterification, dual modification, oxidation, and grafting . Starch N-bromosuccinimide) and PPh3 (triphenylphosphine) are generally used to activate the primary hydroxyl in polysaccharide [3P, together with the by-products of the reaction, are difficult to completely remove during products separation [2-) according to SN2 reaction. Afterwards, azide starch reacted with some terminal alkynes through a “click” reaction, thus introducing 1,2,3-triazole to starch. Finally, N-alkylation of the triazole group was carried out, making starch positively charged.The concept of “click” reaction was first introduced by Sharpless et al. in 2001 . Cu(I) cccharide . Howeverccharide . Moreoveparation . Previouparation ,29,30. TN-alkylation. The structures of the obtained derivatives were analyzed using FTIR and 1H NMR. The antioxidant activities of the synthesized derivatives were evaluated by testing their radicals scavenging activities against hydroxyl radicals, DPPH radicals, and superoxide radicals. Some explanations about the structure and activity relationship were presented. The biocompatibility of the cationic starch derivatives was confirmed by the low hemolytic rate (<2%).This study mainly aims to improve the antioxidant activity of starch, thus providing theoretical support for the development of starch-based antioxidant materials utilized in the food and biomedical fields. For this purpose, a convenient method for pre-azidation of starch was developed. A total of five novel cationic 1,2,3-triazole functionalized starch derivatives were synthesized by using a “click” reaction and 4 Da, was purchased from Sinopharm Chemical Reagent Co., Ltd. . N-bromosuccinimide (NBS), triphenylphosphine (PPh3), and terminal alkynes including 2-propyn-1-ol, 3-butyn-1-ol, 3-butyn-2-ol, 4-pentyn-1-ol, and 5-hexyn-3-ol, were all purchased from the Sigma-Aldrich Chemical Corp . Ethanol, chloroacetyl chloride, triethylamine, dimethylsulfoxide (DMSO), and N,N-dimethyl formamide (DMF) were all purchased from Sinopharm Chemical Reagent Co., Ltd. . The reagents were of an analytical grade and were used without further purification.Potato starch with a weight-average molecular weight of 9.8 × 10−1, using transmittance mode at a resolution of 4.0 cm−1.Fourier Transform Infrared (FTIR) spectra of the samples were performed on a Jasco-4100  Co. Ltd., Shanghai, China). The wave number was recorded in the range of 4000–400 cm1H NMR spectra of the samples were performed on a Bruker AVIII-500 Spectrometer  at 25 °C taking DMSO-d6 as a solvent. The degrees of the substitution of the starch derivatives were determined based on the integration of the 1H NMR spectrum [spectrum .The ultraviolet-visible (UV-vis) spectra were recorded on a UV-vis spectrometer .CASC was synthesized according to Tan’s methods . Starch A mixture of CASC (10 mmol) and sodium azide (30 mmol) were dissolved in 100 mL of DMF. The reaction was carried out at 80 °C for 4 h. Afterwards, the crude product was precipitated in excess ethanol. The solid was filtered and washed with ethanol then dried at −53 °C under vacuum to obtain azide acetyl starch (AASC) .A mixture of AASC (1 mmol AGU), TEA , cuprous iodide , and terminal alkyne (3 mmol) dissolved in 25 mL of DMSO was stirred at 75 °C under argon atmosphere for 24 h to complete the reaction. The crude product was precipitated in excess ethanol and further purified in a Soxhlet apparatus with acetone for 48 h. The precursor starch derivatives (1a–1e) were obtained by freeze drying at −53 °C in vacuum .Above 1,2,3-triazole functionalized starch derivative  was dissolved in 20 mL of DMSO. The solution was added dropwise with iodomethane (5 mmol), then warmed to 60 °C and vigorously stirred at reflux for 24 h. Afterwards, the solution was poured into excess acetone to isolate the product. After the solid was filtered and washed with ethanol and acetone three times, it was dialyzed against deionized water for 2 days to purify the product. The cationic 1,2,3-triazole functionalized starch derivatives (2a–2e) were obtained by freeze drying at −53 °C in vacuum . SD, n = 3). The water solubility (S) of starch and its derivatives were calculated by the following equation:1 means the weight of the undissolved parts (mg).The test of water solubility was performed by the previous methods . A totalIn general, antioxidant research is divided into in vivo antioxidant activity and in vitro antioxidant activity. Antioxidant research in vivo is based on antioxidant activity in vitro . There a−1, 0.9 mg mL−1, 1.8 mg mL−1, 3,6 mg mL−1, and 7.2 mg mL−1) mixed with EDTA-Fe2+ , potassium phosphate buffer , safranine T , and H2O2 , reacted at 37 °C for 30 min. The UV absorption of the mixture at 520 nm was measured subsequently. Three replications for each sample were performed and the hydroxyl radical scavenging ability was calculated using the following equation:Asample 520 nm was the absorbance of the sample, Ablank 520 nm was the absorbance of the blank group (distilled water replaced samples), and Acontrol 520 nm was the absorbance of the control group (distilled water replaced H2O2).Hydroxyl radical scavenging assay was based on the Fenton oxidization . Briefly−1, 0.6 mg mL−1, 1.2 mg mL−1, 2.4 mg mL−1, and 4.8 mg mL−1) mixed with 2 mL of DPPH solution , was incubated at 25 °C for 30 min. Subsequently, the UV absorption of the mixture at 520 nm was recorded. Three replications for each sample were performed and the DPPH radical scavenging ability was calculated by the following equation:Asample 517 nm was the absorbance of the sample, Acontrol 517 nm was the absorbance of the control (ethanol replaced DPPH solution), and Ablank 517 nm was the absorbance of the blank (distilled water replaced the samples).The study of the DPPH radical scavenging ability was conducted using the methods developed by Tan . In brie−1, 0.6 mg mL−1, 1.2 mg mL−1, 2.4 mg mL−1, and 4.8 mg mL−1) was seriatim added with NADH , NBT , and PMS . The mixed solution was kept in dark at room temperature for 5 min. The UV absorption was measured at 560 nm. The test was repeated for three times at the same conditions. The superoxide radical scavenging ability was calculated using the following equation:Asample 560 nm was the absorbance of the sample, Acontrol 560 nm was the absorbance of the control (distilled water replaced NADH) and Ablank 560 nm was the absorbance of the blank (distilled water replaced the samples).The superoxide radical scavenging assay was performed following the methods developed by Tan . Each saThe hemolysis assay was performed following reference  with appp < 0.05, the results were considered statistically significant.The data of the antioxidant activities were processed by Excel, Origin, and SPSS to provide the results. The data were analyzed using an analysis of variance. When 1H NMR spectra (The structure of starch and its derivatives were characterized by using FTIR  and 1H N spectra .ν(O–H) 3428 cm−1, ν(C–H) 2927 cm−1, δ(O–H) 1427 cm−1, δ(C–H) 1373 cm−1, and an absorption band ν(C–O) and ν(C–C) 1200–990 cm−1. After the chloride acetylation, a new peak at 1748 cm−1 was assigned to ν(–COO–) of the chloroacetyl group. Regarding AASC, a strong peak at 2112 cm−1 was assigned to the azide group. For 1a–1c, the peak at 2112 cm−1 disappeared completely, because the azide group in AASC was transformed to 1,2,3-triazole, which indicated the “click” reaction was successfully carried out.1H NMR spectra in 1H NMR spectra of CASC, which was contributed to the hydrogen of –CH2Cl. In the 1H NMR spectra of AASC, the new signal at 4.1 ppm belonged to the hydrogen of –CH2N3, indicating that the azide group had been successfully grafted onto starch. In the 1H NMR spectra of 1a–1c, the hydrogen at C5 position in the triazole ring showed absorption at 7.8–8.0 ppm, indicating the successful introduction of 1,2,3-triazole group to starch. In addition, the hydrogen of methylene between triazole and ester group showed absorptions at around 4.9 ppm. Other methylenes also showed signals at 4.5 ppm (in 1a), 2.8 ppm and 4.5 ppm (in 1b), 1.4 ppm and 4.5 ppm (in 1c), 1.7 ppm, 2.6 ppm, and 4.5 ppm (in 1d), and finally 0.9 ppm, 1.3 ppm, 1.7 ppm, and 4.5 ppm (in 1e). After alkylation with methyl iodide, some changes of the chemical shifts could be observed from the figure. For example, the signal of the hydrogen at the C5 position in the triazole ring disappeared at 7.8–8.0 ppm, and instead appeared at 8.7–8.9 ppm. A new signal at around 3.4 ppm in 2a–2e contributed to the hydrogen of methyl in the cationic 1,2,3-triazole group. In addition, all the signals of methylene mentioned above were moved compared to those signals in 1a–1e.1H NMR spectrum. This was based on the position and intensity of the signal of the α-anomeric hydrogens (H1) at 5.1 ppm [In this study, the DS of starch derivatives (2a–2e) was calculated by using the  5.1 ppm . The resThe water solubilities of starch and its derivatives, as well as the intermediates in distilled water at 25 °C were summarized in 2O2 by the Fenton reaction [2) in cell metabolism [It is generally recognized that the hydroxyl radical is generated from Hreaction . Hydroxytabolism . It is atabolism , and is tabolism . Figure DPPH radical is a very stable nitrogen-centered free radical due to the conjugation and steric hindrance effect, which is considered as one of the standard and easy colorimetric methods for the evaluation of antioxidant properties of polysaccharide . Under aThe scavenging abilities of starch and its derivatives against DPPH radicals are shown in 2O2, causing damage to cell structures and functions [Superoxide free radical is a kind of free radical produced in the metabolic process of organisms. It can directly attack biological macromolecules and additionally act as a precursor of hydroxyl radical and Hunctions . Superoxunctions .−1, all the derivatives including 1a–1e and 2a–2e could completely scavenge the superoxide radicals.The superoxide radical scavenging abilities of the synthesized starch derivatives are shown in The synthesized cationic starch derivatives (2a–2e) with pronounced antioxidant activity compared to native starch have great potential when utilized in the food and biomedical fields. As a result, the hemolysis assay of the derivatives as a preliminary assessment of the biocompatibility is necessary. The results of the hemolysis assay are shown in N-alkylation. A convenient method for pre-azidation of starch was developed. The structures of the obtained derivatives were analyzed using FTIR and 1H NMR. Their antioxidant activities were evaluated by testing their radicals scavenging activities against hydroxyl radicals, DPPH radicals and superoxide radicals. All the starch derivatives including 1a–1c and 2a–2c exhibited pronounced antioxidant activity compared to pure starch and they are particularly effective against superoxide radicals. These results demonstrated the advantages of chemical modification in improving the antioxidant activity of starch. The improved antioxidant activities of the derivatives probably benefited from the enhanced water solubility and the added positive charge. The biocompatibility of the cationic derivatives was confirmed by the low hemolytic rate (<2%). The starch derivatives described in this study have great potential as antioxidant materials for food and biomedical applications.In summary, five cationic 1,2,3-triazole functionalized starch derivatives were synthesized by using the “click” reaction and


Experimental tests were performed on a stack of cuboidal steel elements and its mathematical model was developed. Each cuboidal element was made of rolled profiles. Such stacks are the part of temporary supports that can be used to eliminate deflections of buildings. Stacks were loaded eccentrically through the inaccurate position of a jack. Moreover, two types of geometrical imperfections could be noticed. They included inaccurate contact between the stack elements and initial relative displacements of profiles that formed cuboidal elements. A mathematical model was developed to describe deformations of the stack and its parameters were determined by analysing test results. The eccentricity of the applied force had a slight impact on the stack stiffness, which was considerably reduced by geometrical imperfections. The imperfection covering initial relative displacements of rolled profiles inside cuboidal elements had the greatest impact on the stiffness. It could cause even a 10-fold drop in the stack stiffness when compared with the theoretical stiffness resulting from the stiffness of the stack section, and the stiffness dropped by ca. 3.5 times when the imperfection included the inaccurate contact between the cuboidal elements. Finally, the occurrence of both types of geometrical imperfections generated the real stiffness more than ten times lower than the theoretical stiffness that did not take into account imperfections. Each cycle consisted of loading and unloading the system four times with Q values from 0 to Qmax  to Qmax, and then was decreasing to the value close to zero. The force values Q within the range from 0 to 1.5 kN were neglected during the tests because the elements have not been adjacent to each other under that load yet. The reason was their loose arrangement. The test stand is shown in Q within the range of 0 to 1.5 kN were neglected during the test, taking under consideration the dead weight of the stack.Each test was preceded with a precise, and coaxial arrangement of the stack elements and the arrangement of hydraulic jacks in conformity with the assumed eccentricity Q-Δl during which the lowest and the highest Qmax value corresponding to the given eccentricity ea was observed. Each diagram presents four load–unload cycles. The system returned each time to its initial position, and hysteresis loops were formed. Loops corresponding to a given load Qmax and eccentricity ea overlapped with each other. For this reason, the single hysteresis loop from each test was considered as representative for further analysis. Thus, diagrams in Q-Δl relationships for a given eccentricity ea limited to singular hysteresis loops.Selected test results are shown in Q-Δl cycle. Phase I corresponded to the monotonous increase in the load from zero to Qmax  of the system composed of cuboidal elements. The defined model of the stack should represent load-displacement (Q-Δl) relationships observed during tests conducted on the real stack. A structure of the model was defined at first, and then the equations describing its particular elements were used.The aim of building the mathematical model of the stack was to determine the relationship between load and displacement ust=∑u of the model expressed as (2) corresponded to the change Δl in the length of the real stack. As previously described, each of the displacement components is related to deformations of the material used in the stack. Those deformations are considerably smaller than the plastic deformations. Components ust, ucon, uint of the displacement in the model were elastic displacements. Each of those displacements corresponded to spring deformations in the model that are denoted by kst, kcont and kint. Due to (2), the springs were connected in series, as illustrated in Nint,fr in the model, which represented internal friction.The displacement Q. Friction revealed if the displacement uint occurred. Therefore, the spring kint and the element, in which friction Nint,fr occurred, were connected in parallel. A pair of elements kint and Nint,fr represented the behaviour of the cuboidal element, and the spring kcon specified deformability of the interface between the cuboidal elements. They are elements of the model representing the structural solution for the stack. The spring kst is responsible for stiffness of the stack, for which the structural solutions are not included. Mathematical relationships describing particular spring and internal frictions are described in subsections below.The quasi-static load exerted on the stack did not generate internal friction in the material used to create the stack. There was no likelihood of friction on supporting surfaces in the direction of the load kst corresponded to the stiffness of the stack in the form of a homogeneous rod under compression. Its value was constant and determined as the ratio of change ΔQ of the load value to the adequate change Δust in the displacement:The stiffness kst resulted from the compression of n cuboidal elements connected in series could be expressed as:Assuming the stiffness kcub—compressive stiffness of the cuboidal element,E—modulus of elasticity,lcub—length of the cuboidal element,A—cross-sectional area of the diaphragm part of the cuboidal element.ust of the stack model was regarded as the compressed homogeneous rod loaded with the force Q, without taking into account the structural solution, was equal to:The displacement Q was increasing or decreasing. As mentioned above, the effect of the structural solution on the system stiffness may be evaluated by referring to the real stiffness of the stack to the stiffness kst.The relationship (7) was also applicable when the load value kcon included imperfections, that is, inaccurate contact between supporting surfaces of adjacent cuboidal elements. That value expressed the additional deformability of the stack resulting from non-dilatational deformations of elements forming the stack. The contact area between the cuboidal elements was increasing under an increasing load. It means that the stiffness kcon depended on the value of the vertical load. It was assumed by authors the change could be described with the quadratic function as follows:αcon, βcon, γcon are parameters of the spring kcon, determined from the tests.The stiffness ucon was expressed with the following relationship:Q* is a dummy variable. After taking into account the boundary condition:Therefore, the displacement Q was increasing or decreasing. The reason for that was the lack of the friction force on the contact surface acting in the direction of the load Q.The relationship (12) was also applicable when the load value uint, occurred inside the cuboidal elements during their loading. They are generated by the imperfections in the form of initial relative displacement of rolled profiles. Displacement uint, however, can be identified with a whole group of imperfections, as a result of which there are mutual displacements of profiles inside a single rectangular element when loading the stack. The reason for those imperfections is the inaccurate assembly of rolled elements and deviations in dimensions of the profiles. The presence of those imperfections led to the additional deformability of the stack represented by the spring kint connected in series with other stiffness components of the model. The displacements uint are accompanied by the forces of internal friction acting in the direction of the load. Therefore, the non-elastic element connected in parallel with the spring kint is inserted into the model. The external load Q was balanced by two internal forces: the elastic force Nint,el present in the spring, and the friction force Nint,fr in the non-elastic element. Values of both forces depend on the displacement uint.The relative displacement of rolled profiles, denoted by Q-Δl obtained from the tests, the value of the elastic force was assumed by authors to be described with the exponential relationship:On the basis of the observed relationship By transforming Equation (13), the relationship was obtained:Nint,fr was linearly dependent on the value of the internal elastic force. Sense of the non-conservative force of internal friction was in the opposite sense to changes of the displacement uint. Therefore, Nint,fr was the function of displacement and direction of the velocity:uint. The positive sense of the velocity was considered as a change of increasing value uint.The value of the internal friction The equation of equilibrium of forces in the model of cuboidal elements takes the following form :(16)Q=Niuint, when I) on the basis of (13), (15) and (16), the equation could be expressed as:For increasing displacement uint, when III) by analogy to (19), the equation can be expressed as:For decreasing displacement uint,max when the load of the value Qmax was applied. Then, the load was reduced, which could be interpreted as applying the load ΔQ of the opposite sense to the force Qmax  corresponded to the displacement uint,max.Taking into account that:Q can be expressed as:Taking into account the assumptions (15) and (23), values of internal forces can be expressed as:kint determined as:Stiffness kint in the analysed model was the linear function of the internal force Nint,el. Characteristics of the model describing cuboidal elements are presented in By substituting (14) with (29), the following is obtained:Q-u. At the beginning of the loading process (Q = 0), the displacement was equal to zero (u = 0). The increasing load Q induced the displacements ust, ucon and uint , (11) and (21) defining the displacements u, ucon and uint. Moreover, the model stiffness increased. The displacement reached the highest value umax under the load Qmax. That part of the relationship Q-u corresponded to Phase I.The assumed model was used to determine the relationship and uint . GreaterQ, only the springs kst and kcon deformed. The force Nint,fr, being the force of kinetic friction in that stage, prevented the occurrence of the displacements uint. Displacement of the model in that phase was the sum u = ust + ucon. Hence, a considerable decline of the value Q at a relatively small change in the displacement u occurred. The part of the relationship Q-u, at which the load Q decreased by the value ΔQ was Phase II of loading, for which the load ΔQ was determined from (24).In the initial phase of decreasing load Q = 2Nint,fr, the sense of the friction force changed. A further drop in the value Q was accompanied by displacements of all components of the displacement u and a decline in the non-elastic force Nint,fr. That force, which was again the force of kinetic friction in that stage, performed work on the displacement within the range from uint,max to 0. Because the displacements ust, ucon and uint were revealed in the model, a drop in Q at the displacement change u was considerably smaller. The stiffness of the whole model of the stack was lower than in Phase I, in which three components of the displacement  occurred, because a greater internal force was acting during unloading of the model in the spring kint than during loading. That was caused by relative senses of the forces Q, Nint,el and Nint,fr in Phase III . The non-linear spring kcon was characterised by three parameters  and the non-linear spring kint by two parameters (αint and βint). The element, in which the friction force Nint,fr acted, was described by one parameter (αfr). Thus, the assumed model was characterised by seven parameters. The purpose of further analysis was to determine values of those seven parameters on the basis of results from the experimental tests discussed in The defined model of the stack was composed of four elements: one linear spring, two non-linear springs, and the element, in which the friction force acted. The linear spring was characterised by one parameter . For that purpose, the parts of that element were distinguished . Assuming the length of the cuboidal element (lcub = 72.5 mm) on the basis of (6), the following was determined:The total width of those elements equalled to 81.6 mm. The product of the width and height of the section of the cuboidal element (350 mm) was the area kst was determined on the basis of (5) as equal to:Taking into account the number of cuboidal elements, the stiffness kst was the theoretical stiffness of the stack, in which its structural solution and its imperfections were not included.That stiffness was the constant value, independent of the load and the eccentricity a. It shoII was taken into account to determine parameters of the spring kcon including the presence of contact area between adjacent cuboidal elements. Using the test results (graphs Q-Δl), the relationship Q-u in that phase was approximated using the straight line (Phase ght line :(33)Q=kIkII were determined assuming the straight line (33) passed through points being the top and bottom boundary of Phase II. Those values were found out for all conducted tests and they are presented in II occurred only for two springs: kcon and kst. They were connected in series and had approximate equivalent stiffness kII, which could be expressed as:Values kst was known, the relationship (34) was the base to determine the stiffness kcon for all conducted tests. Approximate stiffness values kcon calculated for a given test are shown in kcon as a function of the loading force for a given value of the eccentricity ea. The relationship was determined using the method of least squares. The obtained parameters αcon, βcon, and γcon achieved satisfactory compatibility—the value of determination coefficient R2 was not smaller than 0.991. The relationships describing the stiffness kcon corresponding to particular eccentricities are illustrated in the graph  were determined using the method of least squares. Similarly, the tested relationship Nint,fr-uint, making half of the difference in the relationship Nint,el-uint, between Phase I and Phase III was determined to obtain the parameter αfr (αfr of the approximation in the form of (15) was determined using the method of least squares.Determination of the stiffness hase III . Then, teter αfr . Then, tea, and obtained parameters and functions describing the stiffness kint and Nint,fr for individual eccentricities are presented in The procedures described above were applied to all tested eccentricities The defined model confirmed the presence of three phases in the loading-unloading process for the model. Each phase displayed linear-elastic properties resulting from the section stiffness of cuboidal elements and non-linear elastic properties and friction representing the effect of geometrical imperfections. The model was characterised by seven parameters, the values of which were determined as described in Q-Δl obtained from testing the relationship Q-u and from the model were included in the graph. The relationships used to prepare graphs Q-u are compared in Q-u (the model) and Q-Δl (the tests) were practically overlapping. Consequently, the assumed model seemed to describe effects observed in the stack satisfactorily.Graphs were prepared to compare results obtained from the model and test results as illustrated in kst(Q), kcon(Q) and kint(Q). Each subsequent graph corresponds to the stacks loaded at individual eccentricities. Values of the model properties calculated for selected load values are presented in kst was the stack stiffness resulting from the section of cuboidal elements taking part in transferring the force Q in the longitudinal direction. That stiffness value was constant and equal to 6211 MN/m. It was independent of the eccentricity of the applied force and its value. As described above, it was also the theoretical stiffness that could be characteristic for the stack, in which deformations related to the structural solutions and imperfections did not occur.The impact of imperfections on the stack stiffness was analysed by testing the characteristics of springs being components of the model. The graphs in Q equal to zero, the stiffness kcon was 20 MN/m. An increase in loading caused an increase in the value kcon described by the quadratic function. The value kcon was smaller for the stacks with greater eccentricity. Under the load Q equal to 500 kN, kcon was 2498 MN/m at the eccentricity ea equal to zero, and at the eccentricity ea equal to 30 mm, the stiffness was 1273 MN/m  on the stack stiffness in Phase II can be described by the ratio kst,con/kst. The graph representing the ratio for all stack models is shown in Q. For Q = 0, its value was close to zero, and for Qmax = 1000 kN the value was equal to 0.55.A change in the stiffness kst,int for all eccentricities is plotted in kst,int are almost equal to the values of equivalent stiffness kI,III for eccentricities greater than 20 mm. The effect of the imperfection of inaccurate contact between the stack elements (kint) on the equivalent stack stiffness kst,int can be described by the ratio kst,int/kst. The ratio for all stack models is shown in Q. For Q = 0, its value was close to zero, and for Qmax = 1000 kN the value was equal to 0.16.A change in the stiffness kI,III and kII unambiguously indicated the stack stiffness was predominantly affected by the imperfection related to initial relative displacements of rolled profiles inside the cuboidal element. As a consequence, internal non-elastic forces were observed. Those forces also represented the structural friction between displacing rolled profiles that formed cuboidal elements. The presence of those forces resulted in the formation of hysteresis loops in the model. Values of those forces linearly depended on the load value and amounted to ca. 10–12% of the applied load at the eccentricity ea ≤ 30 mm, and 1.7% at the eccentricity ea = 40 mm. The eccentricity of 30 mm or smaller had a slight effect on the non-elastic internal force in the stack.The analysis of equivalent stiffness values The stacks of cuboidal elements used in, among others, rectification processes of vertically deflected buildings, are loaded eccentrically and have geometrical imperfections. The eccentric load of the stack results from the inaccurate assembly of the jack on the stack. The imperfections result from the structural solution. Two types of imperfections were distinguished. The first type included the inaccurate contact between the cuboidal elements, and the second one referred to initial relative displacements of profiles inside the cuboidal elements. Those imperfections were the consequences resulting from deviations in dimensions of rolled profiles and the inaccuracy of relative position of profiles during welding.The tests on the equivalent system were conducted under laboratory conditions to represent the behaviour of a 1 m long stack that was the component of the building support. Cyclic loading of the stack was made at the test stand. The force was applied with the eccentricity equal to 0, 10, 20, 30 and 40 mm. The load increased from 1.5 to 1000 kN. Deformations of the material were found to be elastic, whereas the relationships between the force and the change in the stack length were non-linear. Moreover, the hysteresis loops were observed. However, the system returned to its initial position after its unloading. It meant that apart from elastic forces, internal forces of the friction acted in the stack. They were responsible for activating or deactivating relative elastic displacements of rolled profiles that formed the cuboidal elements. At the beginning of loading (1.5 kN), the stack stiffness, depending on the eccentricity, ranged from 2 to 9 MN/m, under the load of 500 kN from 447 to 549 MN/m, and under the load of 1000 kN from 881 to 946 MN/m. Slightly higher values of the stiffness were obtained for greater eccentricities.The mathematical model of the stack representing its deformations during the load-unload cycle within the range of 1.5–1000 kN was composed of four elements: one linear spring, two non-linear springs, and the element, in which the friction force acted. The linear spring represented the stiffness of the stack regarded as the rod of the equivalent section. The first non-linear spring described by an increasing quadratic function in a function of loading represented displacements corresponding to the imperfections of inaccurate contact between the cuboidal elements. The second non-linear spring described by an increasing linear function modelled the displacements corresponding to the imperfections of initial relative displacements of rolled profiles that formed the cuboidal elements. The springs in the defined model were connected in series, and the element, in which the forces of static friction were observed, was connected in parallel with the second non-linear spring. The model was described with seven parameters, in which the values were determined on the basis of the tests.I). At the beginning of unloading (Phase II), the friction forces prevented displacements in the second non-linear spring. Hence, the model had a four times greater stiffness than in the final phase of unloading when three springs were again deformed (Phase III).Three springs were displaced during loading of the model (Phase I and III slightly increased the stack stiffness by ca. 4–10% when increasing the eccentricity by 10 mm. The eccentricity increase in Phase II by 10 mm caused a drop in the stack stiffness by ca. 11–22%.The analysis of the model demonstrated the eccentricity values in Phases The presence of imperfections in the stack considerably decreased its stiffness. However, that effect was decreasing as the load was increasing. Stiffness of the stack with a length of 1 m, regarded as the system without any imperfections, was equal to 6211 MN/m. The imperfections of initial relative displacements of rolled profiles forming the cuboidal elements had the greatest impact on the stack stiffness. For example, the imperfections caused a 10-fold drop in the stack stiffness under the load of 500 kN, and the stiffness dropped by ca. 3.5 times when the imperfections consisted in the inaccurate contact between rolled profiles. Finally, the combined imperfections caused almost a 14-fold drop in stiffness of the stack with a length of 1 m under the load of 500 kN from 6212 to ca. 450 MN/m.


Defined by Despite personnel problems and production issues due to the pandemic, we will complete publication of all 2020 issues by March of this year. As we face a change in our distributor due to the economic downturn, we look forward with expectations for a new editor and distributor. Our recent member survey highlighted membership support in favor of the organization having a journal and individuals were willing to support it with their membership fees and some willing to add to that support with their own finances. Because of this mandate we will continue to explore ways to produce the journal. A seasoned editor will be leading the new task force to determine where to go from here.Looking back on the bright side, we offered our first student scholarship for best abstract. Congratulations to the honoree! Other good news is the re-indexing by SCOPUS in addition to EBSCO, PMC, and DOAJ. Finally, we have had consistent submissions by members and nonmembers and for a small society and fledging journal we are doing all right.As founding editor and editor-in-chief for the past six years, I’m proud of how the journal has progressed despite our many challenges and happy that it provides a venue for our members and nonmembers to contribute to knowledge regarding research, practice, education, and policy on Asian and Pacific Islanders.Wishing everyone a safe, healthy, and happy 2021. Aloha nui loa and A hui hou kakou.Jillian Inouye, PhD, FAANEditor-in-Chief


The stiffness response or load-deformation/displacement behavior is the most important mechanical behavior that frequently being utilized for validation of the mathematical-physical models representing the mechanical behavior of solid objects in numerical method, compared to actual experimental data. This numerical study aims to investigate the linear-nonlinear stiffness behavior of carbon fiber-reinforced polymer (CFRP) composites at material and structural levels, and its dependency to the sets of individual/group elastic and damage model parameters. In this regard, a validated constitutive damage model, elastic-damage properties as reference data, and simulation process, that account for elastic, yielding, and damage evolution, are considered in the finite element model development process. The linear-nonlinear stiffness responses of four cases are examined, including a unidirectional CFRP composite laminate  under tensile load, and also three multidirectional composite structures under flexural loads. The result indicated a direct dependency of the stiffness response at the material level to the elastic properties. However, the stiffness behavior of the composite structures depends both on the structural configuration, geometry, lay-ups as well as the mechanical properties of the CFRP composite. The value of maximum reaction force and displacement of the composite structures, as well as the nonlinear response of the structures are highly dependent not only to the mechanical properties, but also to the geometry and the configuration of the structures. As a widely used material in advanced industries—such as aerospace, automotive, etc.—fiber-reinforced polymer (FRP) composites have been the subject of many studies ,2. Most In many case studies—including material or structures at different micro-macro scales—the validation of FE models has been frequently done by comparing the stiffness curve of solid objects that obtained through FE simulation and compared with their actual behavior measured in the experiments or numerical approach ,11,13. TNumerical modelling of the elastic-damage behavior of composite materials and structure is of importance, because the occurrence of invisible damage inside the composite materials and structures such as delamination, fiber breakage, and matrix cracking could cause catastrophic failure of the whole structure ,21. SeveAlthough the theory of composite materials and damage models are used to obtain the stiffness behavior of the structure; however, no studies have been implemented to investigate and describe the stiffness response of the composites in detail and its dependency to the elastic-damage properties at material and structural levels. In this regard, the suitability and applicability of a composite structures to be used for validation of a new damage model or characterization of the mechanical properties through numerical approach, is not determined. On the other hand, the effectiveness of each properties in determination of the mechanical behavior at structural level, has not been studied. Therefore, this study aims to investigate the linear-nonlinear stiffness responses of a carbon fiber-reinforced polymer (CFRP) composites at two levels of material and structure, and describe its dependency to elastic and damage model parameters. For this purpose, four FE models of different composite cases  are selected for simulation at full damage and failure states, in which the effect of sets of individual and group of elastic and damage parameters are examined. In total, 48 FE models representing the combination of different specimens and properties are run and the results are compared systematically, to provide a comprehensive analysis of stiffness response dependency to the mechanical properties. The results provide important insight into the analysis of composite structures when used for examination of design cases, validation of new theoretical models, and characterization of the mechanical properties.In modelling aspect, each lamina is treated as a homogeneous orthotropic layer. Classical lamina theory is used to model composite lamina . To descrd tests , are userd tests ,34.There are several constitutive damage models for defining the FRP composites failure ,36,37,38The initiation of damage is predicted by the Hashin’s failure model in laminas at meso-scale . The strFor the fiber fracture, buckling or kinking: For the matrix cracking and crushing: T, YT, XC, YC, SLX, and TS are the strength properties, and the parameters In these equations,  samples .D is the damage variable matrix, which obtained from the strain equivalence hypothesis, as The result indicated a direct dependency of the stiffness response of the composite at material level to the mechanical properties. However, the stiffness behavior of the composite structures depends both on the structural configuration, geometry, lay-ups as well as the mechanical properties of the CFRP composite. The elastic properties directly affect the initial slope and linear response of the composite at material level. However, the composite structures showed two slopes in the linear part of the stiffness curve, in which the initial slope (e.g., Since the stiffness curve is frequently used in the design and analysis of advanced structures, structure cases similar to the case in this studies are introduced as good examples, if the objectives of a research is the investigation of a new theoretical modeling or characterization of FRP composites to highlight the accuracy of material model and parameters (e.g., thick composite plate,


The accurate modeling of the complex dynamic stiffness of inflated rubber diaphragms in pneumatic springs is necessary for an efficient design of vibration isolation tables for precision instruments, such as optical devices and nano-scale equipment. In addition to pressurized air, rubber diaphragms, essentially employed for the prevention of air leakage, make a significant contribution to the total complex stiffness. To reflect the effect of the dynamic stiffness of the inflated rubber diaphragm on the total complex stiffness during the initial design or design improvement stage, it is desirable to predict the complex stiffness of the inflated rubber diaphragm beforehand. In this paper, an estimation method for the complex stiffness of inflated rubber diaphragms using the commercial finite element method  is proposed. The proposed method reflects their dynamic characteristics under the large static deformation by the Mooney–Rivlin and Morman’s constitutive equations. The results of comparison with experimental results indicate that the predictions obtained by the proposed method are congruent with the experimental values of the diaphragm. The vibration environments for precision instruments, such as optical devices and nano-scale equipment, are frequently composed of pneumatic springs, which have lower stiffness than conventional rubber or coil springs. However, as the vibration criteria for precision instruments ,2,3 becoA pneumatic spring comprises a piston and a diaphragm to enclose and seal the pressurized air inside a chamber and to guide the principal axis of the pneumatic spring. The rigid piston supports the payload mass consisting of an isolation table and/or a precision instrument. The diaphragm, a rubber membrane with a relatively complicated shape, is installed for the prevention of air leakage. Therefore, the air in the pneumatic chamber eventually works together with the diaphragm as a stiffness element when the vibrations of the base or payload cause the compression/expansion of the air.Harris et al.  and DeBrResearch on the overall analysis method and procedure including the behavioral characteristics of rubber using FEM is being carried out in various fields as FEM analysis ,17,18 anIn our earlier research ,26, we iThe schematic diagram of a pneumatic spring is shown in In this study, two stages of FE analysis  were employed for to calculate the diaphragm complex stiffness. The objective of the nonlinear static analysis is to obtain the equilibrium configuration of the inflated rubber diaphragm after pressurization. In the linear dynamic analysis, the inflated rubber diaphragm under static equilibrium was sinusoidally excited to calculate its complex stiffness. An analysis procedure using ABAQUS, the most widely used code among commercial FE analysis codes, is proposed. The overall procedure is summarized in λ( = 1 + ε) is related to the engineering strain ε. In Equation (1), the coefficients C1 and C2 are constants to be determined from the static test data.Because the diaphragm presented in In the case of contact condition, this work adopts the standard contact model that is readily available in ABAQUS software. In other words, it is simply performed by leaving all relevant parameters as default. The reason why the default is used and it is relatively neglected is twofold, as follows. One, the frictional characteristic between the contact pairs  was considered to be negligible. In other words, the walls do not seriously affect the deformation of the diaphragm, but only prevents the diaphragm from inflating in an outward direction. Another is concerned with the composition of the diaphragm. It is mainly made up of rubber material, but also reinforced by a thin fabric. The role of fabric definitely is to restrict an excessive inflation of the diaphragm, which relieves the interaction between the contact pairs.λ = 1.05–1.3, and stress measurements for each stretch were made after a 20-min relaxation. The square boxes in C1 and C2. The least square fit with Equation (1), when the values of C1 and C2 are 8.7 and −0.8 MPa, respectively, is shown as a solid line in C1 and C2 were previously derived through static experiments. The relationship between One of the famous constitutive equations used to analyze the dynamic behavior of viscoelastic materials in the commercial FE analysis is the Morman model . The Mor follows :(3)1+jω0The behavior of viscoelastic materials depends on various factors, such as dynamic amplitude, static preload, and frequency. Because it is difficult to review all dependencies, this study considered only the dependence due to the frequency- and dynamic amplitude-dependent characteristics. The dynamic amplitude for a simple-shaped specimen should be defined when extracting the dynamic modulus to reflect the dependence on the dynamic amplitude.ε0, which corresponds to the pressure-induced static deformation, should be conducted in the dynamic analysis stage. In this study, the following scheme was used to determine the pre-strain ε0 in the dynamic characterization:s:diaphragml and ldiaphragm0: are the statically deformed and initial length of the diaphragm, respectively. In the same manner, the input dynamic strains dε for the dynamic characterization can be resolved as follows:Sinusoidal displacement excitations were applied to the piston side of the statically deformed diaphragm obtained from the static analysis, while calculating the output force at that point, as depicted in d:diaphragml in the above equation denotes the deformed length of the diaphragm under dynamic loading, as expressed in d:diaphragml cannot be precisely known without complex modulus data to be measured. To obtain the approximate value of d:diaphragml, instead, a secondary static analysis applying the dynamic displacement amplitude at the piston side was performed. pX used in the secondary static analysis and the resulting dε for the dynamic characterization of the specimen.dε superimposed on the pre-strain ε0 (11%) of the specimen, measurements of the complex modulus between 0.2 and 25 Hz were made, as presented in By using the values of The complex modulus data were applied to the complex stiffness calculation of the inflated rubber diaphragm. For reference, different complex modulus data, which were obtained by the consideration of the static-strain distribution in the diaphragm, needed to be employed for each element of the FE model. This process may improve the quality of FE results, because the static-strain distributions of the diaphragm are not uniform. However, it is tedious and time-consuming in the state of the art of commercial FE method. Nonetheless, a systematic approach  can assiThe results calculated using ABAQUS, a commercial FE analysis code, are shown in sk from the measurements of the pneumatic spring as follows:ω- and dynamic amplitude pX-dependent characteristics. The stiffness of air sk, which is essential for the extraction of the complex stiffness of the diaphragm, is shown in Equation (7). Full derivation of sk requires consideration of the first law of thermodynamics and the ideal gas law in the pneumatic chamber. More details can be found in reference [As mentioned in the introduction, the diaphragm will deform together with the pressure change inside the chamber. That is, the measurements of the complex stiffness of the pneumatic spring contain effects of the air in the chamber and diaphragm in parallel, as shown in eference .(7)ks=κ0 and V0 designate the supplied pressure and chamber volume, respectively, both of which are obtainable from direct measurements. Finally, pA represents the equivalent piston cross-sectional area under the assumption that the dynamic behavior of the piston and diaphragm can be represented by that of a single piston.κ(=1.4) in the above equation denotes the specific heat ratio. ppA can be estimated with reference to pdx. The volume variation of the top chamber tdV is approximated to be a part of a cone with cross-section ABCD, as depicted in the figure. Thus, the equivalent piston area pA is obtained by dividing tdV by pdx as follows:Using the variables described above and Equations (6)–(8), the complex stiffness of the diaphragm can be measured indirectly.0 was installed in the INSTRON dynamic material testing system (model: 8502) driven via computer-controlled servo-hydraulic actuation systems. The displacement and force signals were measured by linear variable differential transformer and load cell, respectively. The measured signals were post-processed to obtain the complex stiffness. The thick line in 0). Various sinusoidal displacement excitations, all of which are the same as those in the material characterization  is shown as solid lines in These observed behaviors of the complex stiffness of the inflated rubber diaphragm approximate the typical characteristics of viscoelastic materials ,30. FromThe calculation results using the FE method are signified by dotted lines in The calculated errors by the above equations are described in This paper discussed how to compute the complex stiffness of an inflated rubber diaphragm using commercial FE method, in which two stages of FE analysis  were employed. The calculated results were compared with the indirectly measured results obtained through the subtraction of the stiffness of the air in the chamber from the measured complex stiffness of the pneumatic spring. The real part of the complex stiffness, which is of primary importance in the improvement of the vibration isolation performance in the pneumatic spring, was matched well (the discrepancies ranged from 4% to 15%), and the frequency and dynamic amplitude dependency was well described through the proposed method. Thus, the calculation method proposed in this paper can be reasonably used for the calculation and prediction of the diaphragm complex stiffness, and it can be employed in the early design stage of pneumatic springs.