Polysaccharides can be used as fat replacers in ice cream, as they contribute to an increase of viscosity. However, no research has clarified the exact role of viscosity from that of the structure of the polysaccharides on the properties of ice cream. In this study, the effect of polysaccharide structure on different properties of low-fat ice cream was investigated. The polysaccharides taken into consideration varied from flexible (locust bean gum and guar gum) to rigid (xanthan gum and iota carrageenan). Relationships between rheological properties of ice cream mixes and microstructural characteristics and sensory perception of the final ice cream were established. To separate the effect of the polysaccharide structure from that of viscosity, two series of ice cream were prepared: one in which the mix viscosity of the various samples was similar (approximately 68.3 mPa· s), and one in which the serum phase viscosity was similar (approximately 15563 mPa· s). Flexible polysaccharides showed a lower degree of shear-thinning and a more liquid-like viscoelastic behavior compared with rigid polysaccharides. In addition, flexible polysaccharides led to higher overrun (47–58%) than other samples (approximately 30%), which resulted in lower hardness of the ice cream (<3.2 MPa). Rigid polysaccharides caused gelation of the serum phase, which made the ice cream more difficult to scoop. Based on the results of the sensory evaluation, flexible polysaccharides could provide higher softness and creaminess-related properties, while rigid polysaccharides resulted in higher coldness and grittiness. Therefore, polysaccharides with a flexible structure are a better choice for improving the textural and sensory properties of low-fat ice cream.  •Effect of polysaccharide on ice cream properties was separated from that of viscosity.•Flexible polysaccharides led to higher overrun and lower hardness of ice cream.•Melting properties were found to relate more to serum phase viscosity.•Flexible polysaccharides could provide higher softness and creaminess.•Rigid polysaccharides resulted in higher coldness and grittiness. However, it contains high amounts of fat, and an excessive consumption of high-fat foods is linked nowadays to obesity and cardiovascular disease, diabetes and cancer. The microstructure of ice cream is rather complex, consisting of air cells, ice crystals, and a network of coalesced fat droplets entrapped in a thick continuous phase . CommercBased on their composition, fat replacers are mainly categorized into three groups: lipid-, protein- and carbohydrate-based . CarbohyBesides viscosity, the sensory perception of ice cream may also be related to the lubrication properties of the different polysaccharides. Even though studies on the tribological properties of individual polysaccharides have been carried out , the lubOur study aimed to shed light on the described issues by investigating the addition of a set of polysaccharides varying in molecular flexibility in fat-reduced ice cream. Two reference samples with 1% and 10% fat were used. Four polysaccharides differing in structure were used to gain insight in how both mix and serum phase viscosity relates to different structural and sensory aspects: locust bean gum, guar gum, xanthan gum and iota-carrageenan. Both locust bean gum (LBG) and guar gum (GG) have a random coil conformation, with a persistence length of approximately 3 nm, and are therefore considered flexible polysaccharides . Xanthan22.1Organic carrageenan-free cream , organic skimmed milk  and sucrose were purchased from a local supermarket . Locust bean gum , guar gum , xanthan gum , iota carrageenan  were purchased from Sigma-Aldrich Chemie GmbH . Vanillin (100%) was purchased from Royal Polak Spices .2.2-1 by varying the concentration of different polysaccharides . In the other series, the serum phase viscosity measured at the mentioned shear rate and at -20 °C was matched in the same way . The sugar concentration in the continuous phase of both series was kept the same (∼15%) to guarantee constant freezing point depression and ice fraction. Vanillin (0.1%) was added to all samples. In addition, two reference samples were prepared, both without polysaccharides: a low-fat ice cream with 1% fat, and a high-fat ice cream with 10% fat. An overview of the model ice cream recipes is shown in Two different series of low-fat ice cream (1% fat) were prepared by using different polysaccharides. In one series, we matched the mix viscosity measured at 50 sFor the preparation of the ice cream mixes, sugar and skimmed milk were first mixed with a magnetic stirrer for 30 min. Then, different amounts of polysaccharides and vanillin were added slowly upon stirring at approximately 25 °C for 1.5 h. The mixed systems were then heated in a water bath at 85 °C for 30 min, to achieve complete dissolution of the polysaccharides. The mixes were cooled to room temperature (approximately 25 °C) and cream was added upon stirring for 1 h before ice cream preparation. The mixes were aged at 4 °C overnight.2.3Ice cream samples (3 L) were frozen in a batch freezer  for 8 min and the draw temperature was approximately -5 °C. The ice cream was collected into different containers depending on the determination to be carried out: plastic rings of 25 mm in height and 70 mm in diameter for hardness and scoopability measurements, metal rings of 5 mm in height and 25 mm in diameter for rheological characterization, and 60 mL containers for sensory evaluation. These samples were first hardened at -20 °C for 24 h to solidify further before measurement.2.42.4.1-1 in a time frame of 5 min. The flow behavior of the ice cream mix was described by a power law model as:n, equal to viscosity when n = 1), γ is the shear rate (s-1) and n, the flow behavior index, is a dimensionless number reflecting the shear-thinning behavior. Lower values of n represent a higher degree of shear-thinning behavior. The measurements were repeated three times for each sample.The viscosity of ice cream mixes was measured using a rheometer  equipped with a concentric cylinder geometry . A sample of 4.7 ml was added to the geometry and the temperature was set at 20 °C. The viscosity was measured at a shear rate ranging from 0.1 to 300 s2.4.2The viscoelastic properties (G' and G") of the ice cream mixes were measured with a rheometer . Prior to measurements, the samples (2 ml) were placed into a PP50 plate/plate geometry and allowed to equilibrate at 20 °C for 120 s. The strain was set at 0.5% and the frequency at 1.6 Hz. Storage modulus (G') and loss modulus (G") were measured during a time frame of 5 min. The measurements were repeated three times for each sample. The mean value of G' of the ice cream mix and the mean value of loss factor of mix (G’’/G') were extracted to characterize the viscoelastic behavior of ice cream mixes.2.52.5.1The overrun of frozen ice cream was determined by first weighing a fixed volume (30 ml) of the aged pre-mix in a metal cup. Next, the same volume of ice cream was weighted in the cup directly after preparation. The overrun was quantified as 2.5.2The analysis of ice crystal size and air cell size was performed according to the methods described by For the analysis of air cell size, a small sample of ice cream was placed on a prechilled microscope slide within an area delimited by a plastic frame  to create a space to prevent the deformation of air cells. The slides were sealed with a cover slide to smear the sample into a thin layer and prevent the escape of air cells. The temperature of the hot stage was adjusted to -6 °C, and at this temperature the air cells were sufficiently buoyant to rise to the bottom of the top slide for size determination.Images of both ice crystals and air cells were taken at 10x magnification to include at least 300 ice crystals or air cells to calculate mean size and standard deviation. The size of ice crystals and air cells was determined by placing a circle around an ice crystal or air cell manually, from which the area and radius were calculated using the software ZEN 2011 assuming that the crystals and air cells were of spherical shape.2.5.3The serum phase viscosity in ice cream cannot be measured directly in the frozen state. Therefore, the serum phase of the various samples was mimicked taking into account the amount of polysaccharides, sugar and other ingredients in the non-frozen fraction. First, the amount of unfrozen water was calculated based on the measured ice fraction. The ice fraction as determined by the Differential Scanning Calorimetry (DSC) was approximately 62% in all samples. Based on this, the concentrations of water-soluble ingredients  in the unfrozen serum phase were calculated. These concentrations were used to recreate the unfrozen serum phase. The exact recipes of the recreated serum phase of all samples can be seen in -1. In some cases, ice crystal formation and growth were still observed, and therefore not all samples could be measured over the entire temperature range. In these cases, the serum phase viscosity was estimated by extrapolation of the results to -20 °C.The serum phase viscosity measurements were performed using a rheometer MCR 501 equipped with a CC17/Ti geometry. During the measuring process, the temperature was decreased from 4 to -20 °C with a cooling rate of 0.5 °C/min. The shear rate was kept constant at 50 s2.5.44,3) was determined separately, and the volume percentage of the aggregated fat clusters was used to reflect the degree of fat destabilization as fat aggregate percentage. To evaluate possible changes in microstructure due to the formation of a polysaccharide, the viscosity of the molten ice cream was compared to viscosity measured before ice cream preparation. The viscosity of the molten ice cream samples was measured at a shear rate between 0.1 and 100 s-1 with a rheometer  equipped with the cup geometry (CC17/T200/Ti). The temperature was set at 20 °C and the total measuring time was set at 5 min. The measurements were repeated three times for each sample. Based on our measurements, both the viscosity of the ice cream mix and that of the molten ice cream showed shear-thinning behavior, indicating that the structure of the samples changed during the shearing process. We took the viscosity of both the mix and molten ice cream at low shear rate (0.1 s-1) to represent the structure of the samples, and their ratio was used to characterize network formation by fat aggregates or polysaccharides, with low values indicating a higher degree of network formation.To evaluate the formation of a fat network, the particle size distribution of ice cream mix and molten full-fat ice cream was measured with static light scattering , using a refractive index of 1.46 and 1.33 for the fat and the water, respectively. A bimodal distribution was observed in the molten ice cream, and according to our previous work , the sec2.62.6.1Hardness and scoopability of the samples were measured with a Texture Analyzer . Samples with fixed volume were prepared using plastic rings of 25 mm in height and 70 mm in diameter. The samples were stored in a freezer at -20 °C for a minimum of 24 h. Prior to measurement, a climate chamber was connected to the Texture Analyzer to maintain a temperature of -20 °C. For hardness determination, the samples were taken out of the plastic rings and were transferred to the climate chamber immediately (-20 °C). Then they were penetrated with an aluminum cylinder probe (5 mm in diameter) attached to a 50 kg load cell to a strain of 50% at a speed of 2 mm/s. Hardness was taken as the maximum stress of the stress-strain curve.To measure scoopability, a scooping spoon probe in combination with an ice cream sample holder was used see , which w2.6.2Temperature sweeps were performed to explore the melting process of the studied ice cream samples by increasing the temperature from -20 to 10 °C. Measurements were performed using a rheometer  at a constant strain of 0.005% and a frequency of 1.6 Hz with a plate-plate geometry (PP50/P2). A moveable hood covering the plate-plate geometry was connected to the cooling system to control the temperature. An air pump was also connected to the hood to prevent heat exchange with the environment. Prior to the measurement, the initial temperature of the plate was reduced to -20 °C using a Peltier element, and then the temperature was increased to 10 °C with a heating rate of 0.5 °C/min. Sixty measuring points were recorded and the total measuring time was 60 min. Storage modulus (G') and loss modulus (G") were measured. Three zones could be identified during the whole process  and two 2.6.3Tribology measurements were carried out to evaluate the lubrication properties of molten ice cream samples. Measurements were performed with a MCR 301 rheometer equipped with a tribology accessory . The set-up was based on a glass ball-on-three-pins principle, consisting of a spherical glass ball (d = 12.7 mm) and three PDMS pins . The temperature of the tribology cup was set at 20 °C and a normal force of 1 N was applied. Samples with a volume of 0.6 ml were poured into the tribology cup, and the friction coefficient was measured as a function of sliding speed. The measurements consisted of two runs in total with increasing and decreasing sliding speed between 0.01 and 470 mm/s, in 7.5 min per run. The data of the second run with increasing speed showed good repeatability and were selected for further analysis. The measured friction coefficient was plotted versus sliding speed. Two regimes could be identified: a boundary regime at low sliding speed, and a mixed regime at intermediate sliding speeds. A hydrodynamic regime was not obtained as the viscosities of the samples were not high enough. The mean friction coefficient in the boundary regime (FCB) and the slope of friction coefficient over sliding speed in the mixed regime (SMR) were used to represent different lubrication properties of molten ice cream samples.2.7A sensory study was performed using an untrained panel . Participants were recruited from the campus of Wageningen University & Research. They all had good general and oral health, good/normal tasting and smelling abilities, used no medication and had no allergies or intolerances towards the ingredients used in the samples. A consent form was signed by the participants and they were given a financial compensation upon completion of the study.A rate-all-that-apply (RATA) methodology was applied, based on previous works in which structural aspects were related to sensory perception for foods in which differences were limited . The sam2.8The data obtained from rheological, tribological and textural measurements were analyzed with SPSS software . The means were compared with a Tukey’s test at a 5% level of significance using an analysis of variance (ANOVA). Moreover, RATA data and overall liking were analyzed using linear mixed models using RStudio with the additional packages LmerTest and Emmeans . Significance level of P < 0.05 was chosen. Additionally, the results obtained from rheological, physical and sensory measurements were analyzed by Principal Component Analysis (PCA) and correlation matrices were established for the different properties. In addition, biplots obtained from PCA were used to visually represent sample grouping and differentiation. PCA analyses were performed using XLSTAT software .33.1-1, 0.55% of LBG and 0.3% of GG were used to obtain a viscosity of approximately 70 mPa· s, whereas only 0.2% of XG and IC were needed to achieve the same viscosity value. This can be explained by the molecular weight and structure of the various polysaccharides. Both LBG and GG have a flexible structure, but the GG used for our research had a molecular weight higher (∼ 4000 kDa) than that of LBG (∼ 310 kDa). Therefore, a lower concentration of GG was required to achieve the desired viscosity. However, the polysaccharide structure seemed to be more important than molecular weight in affecting viscosity. As a matter of fact, a comparable low concentration for XG and IC was required to obtain the desired viscosity (70 mPa· s), even though their molecular weight was quite different . Both XG and IC are more rigid than GG and LBG; they were more effective in increasing viscosity, and therefore their concentration was lower. However, even though the viscosity values were matched, consistency coefficient (K) and flow behavior index (n) were very different, indicating a different shear-thinning behavior for the different polysaccharides. A higher K could be observed in the XG and IC samples, which also proved the higher ability of these polysaccharides to increase viscosity. In addition, smaller n values were found in the samples containing rigid polysaccharides, indicating a greater degree of shear thinning  and loss factor (G"/G') of ice cream mixes were also measured to characterize the viscoelastic properties of ice cream mixes. As shown in 3.23.2.1As shear-thinning behavior and viscoelastic properties of the ice cream mixes were expected to directly affect the structure of the frozen ice cream , overrun, air cell size, ice crystal size and serum phase viscosity were analyzed, and the results are summarized in Compared with the low-fat (1%) reference, increasing fat content and degree of fat destabilization did not appear to influence the overrun. This was consistent with our previous research, which also showed that fat content and degree of fat destabilization did not appear to have a significant effect on the overrun values. This had probably to do with a dominant effect of the freezing process on overrun in our model systems . For the4,3 of the fat particles increased during ice cream preparation from 6.0 μm (ice cream mix) to 47.2 μm (molten ice cream). This value was larger than the critical fat aggregate size (approximately 45 μm) needed to provide a stable 3D fat network, which was obtained from our previous work . In other studies, the size distribution of air pockets was influenced by the viscosity of the ice cream mix , which wous work . In addious work . These dous work . In compTo clarify which properties of the mix were related to the structure of ice cream, correlations between rheological properties of ice cream mixes and different microstructural characteristics of the final ice creams were determined. As shown in In conclusion, ice cream mixes with flexible polysaccharides showed a higher ability to incorporate air cells due to their low shear sensitivity and molecular flexibility. Rigid polysaccharides provided a more solid-like behavior and tended to form a gel network in the serum phase, which was linked to less air incorporation.3.2.2Viscoelastic behavior upon increasing temperature (G' at zone I (G'ZI) and slope of zone II (SZII)), tribological parameters (mean friction coefficient of the boundary regime (FCB) and slope of the mixed regime (SMR)), and textural properties (hardness and scooping energy) of the ice cream samples were determined, as they are important features relating to both the ice cream structure and sensory perception.As shown in The reference with high fat content (10% fat) and the samples with higher serum phase viscosity  showed significantly lower values of SZII, indicating that they melted more slowly. The lower melting in the high-fat (10%) reference could be attributed to the formation of a fat network formed by fat aggregates, which could block the continuous phase between air cells and create a more tortuous path for the serum phase to escape . In addiWe also observed differences in lubrication properties among samples. As expected, the 10% fat sample had a significantly lower friction coefficient in the boundary regime (FCB) and a lower slope of the mixed regime (SMR) compared with the 1% fat sample. As known, fat is a good lubricant, the addition of polysaccharides to the low fat (1%) sample decreased the friction coefficient, especially in the mixed regime. Also a significantly lower slope of the mixed regime was found after adding polysaccharides. In the case of the polysaccharides, the flexible LBG provided the lowest FCB and SMR. However, the lubricating capacity of the flexible polysaccharides (LBG and GG) was not significantly different from that of the rigid polysaccharides (XG and IC). This was unexpected, as it has been reported that stiff and charged polysaccharides provide better lubrication than flexible polysaccharides . This coCorrelation coefficients were determined to evaluate how microstructural elements affected the rheological and physical properties of ice cream with different polysaccharides . OverrunThe melting properties were correlated to the serum phase viscosity, but was less affected by the type of polysaccharide. SZII showed a strong correlation with serum phase viscosity (0.818), but a relatively low correlation with the viscosity ratio, i.e., network formation (-0.307). No microstructural elements showed strong correlations with FCB and SMR. So, next to the fact that the type of polysaccharide was not linked to lubrication properties, also other microstructural elements of ice cream did not directly affect the lubrication behavior.3.33.3.1The results of the sensory evaluation are shown in To get a better understanding of how the sensory attributes were related to the properties of the ice cream, correlation coefficients between physicochemical characteristics and sensory properties are shown in Some strong correlations were found between ice cream characteristics and sensory properties. As expected, overrun was strongly correlated with softness (0.862). This also explained the high negative correlation between G'ZI and softness (-0.878). In addition, overrun was negatively linked to coldness (-0.800). As air is a bad heat conductor, higher overrun leads to slower melting, which has been discussed before by Serum phase viscosity was highly correlated with softness (0.808) and grittiness (-0.869). Therefore, these attributes did not seem to be influenced much by the type of polysaccharides, but just by the viscosity of the serum phase. The positive correlation between serum phase viscosity and softness could be attributed to the fact that a higher serum phase viscosity seemed to be linked to a higher overrun. In addition, the fact that grittiness was inversely related to serum viscosity could be explained by the fact that a more viscous serum phase can mask the detection of ice crystals during oral processing . AlthougAlthough the lubrication properties were not directly linked to structural changes in the ice cream, they were correlated with several sensory attributes. The slope of the mixed regime (SMR) was positively correlated with creaminess (0.812), thickness (0.854), stickiness (0.856), and mouth coating (0.797). These are sensory attributes for which lubrication properties indeed play a role. However, it is worth noting that the friction coefficient in the boundary regime (FCB) did not appear to be closely correlated with any of these sensory attributes, indicating that SMR seems to be more relevant. In previous studies, sensory attributes have always been linked to friction coefficients at a specific velocity, i.e. FCB, and correlations have indeed been observed . HoweverIn conclusion, flexible polysaccharides showed a higher ability to improve the sensory properties of low-fat ice cream than rigid polysaccharides. This seems mostly related to an increased overrun, which was directly correlated to increased softness and reduced coldness. Grittiness perception did not seem to be affected by the type of polysaccharides, but was mainly affected by serum phase viscosity. Attributes as creaminess, thickness and mouthcoating were more related to lubrication aspects (SMR), but could not be linked to the structure of the polysaccharides.4In this study, we investigated the effect of polysaccharide structure on the rheological, tribological, textural and sensory properties of ice cream. Flexible polysaccharides (LBG and GG) were less shear-thinning and showed a more liquid-like viscoelastic behavior compared with rigid polysaccharides (XG and IC), which was reflected in differences in the properties of the ice cream mix. Mixes with flexible polysaccharides showed a higher ability to incorporate air cells, while rigid polysaccharides provided a more solid-like behavior and tended to form a gel network in the serum phase, related to less air incorporation. The higher overrun of samples containing flexible polysaccharides led to lower hardness, while ice creams with rigid polysaccharides were harder to scoop. Overrun and scoopability were thus affected by the type of polysaccharide. However, melting rate was not related to the type of polysaccharide, but to serum phase viscosity. Also, no link between polysaccharide type and lubrication performance was found, nor did other structural ice cream characteristics directly link to lubrication parameters. Based on sensory evaluation, two main groups were found: ice creams with flexible polysaccharides were closer to the full-fat reference ice cream, while those with rigid polysaccharides were closer to the low-fat reference ice cream. Ice creams with flexible polysaccharides were associated with higher softness and creaminess-related properties, while rigid polysaccharides were related to higher coldness. Some attributes did not seem to be directly related to the type of polysaccharide, but to other characteristics. For example, grittiness was more related to serum viscosity, while creaminess, thickness and mouthcoating were related to changes in the friction coefficient. These results indicate that flexible polysaccharides can be considered more suitable fat replacers for improving the sensory properties of low-fat ice cream than rigid polysaccharides.Xiangyu Liu: Investigation, Data curation, Methodology, Visualization, Writing – original draft. Guido Sala: Methodology, Conceptualization, Supervision, Writing – review & editing, Funding acquisition. Elke Scholten: Methodology, Conceptualization, Supervision, Writing – review & editing, Funding acquisition.The authors declare that no competing interest exists.


Surgeons are routinely required to remove loose or failed pedicle screws and insert a new screw in their place. However, inserting a new screw into an existing hole may compromise the holding capacity of the pedicle screw. The purpose of this study is to evaluate the pullout strength of pedicle screws with different thread designs after the primary insertion and revision surgery in a synthetic bone model.Four pedicle screws with different thread designs (single-lead-thread (SLT) screw, dual-lead-thread (DLT) screw, mixed-single-lead-thread (MSLT) screw, and proximal-unthreaded-dual-thread (PUDL) screw) were inserted into pre-drilled, untapped holes  in Sawbone blocks of density 20 pcf. In the first sequence, a 6.0 mm screw was inserted into the predrilled foam block and the primary pullout strength of the screw was measured according to ASTM F543. In the second sequence, a 6.0 mm screw was inserted and removed, and then either a 6.5 mm screw of the same design or a different screw design was inserted into the same hole and the pullout strength recorded.In the first sequence, the mean pullout strength of the MSLT screw was significantly (p < 0.05) greater than all other screw designs. In the second sequence, when the MSLT screw was the primary screw, using a larger MSLT screw (6.5 mm) as the revision screw did not lead to a higher pullout strength than if a 6.0 mm diameter PUDL screw was used for the revision. Using a larger DLT screw (6.5 mm) as the revision screw resulted in a significantly (p < 0.05) greater pullout strength than a 6.0 mm STL, DLT, MSLT, or PUDL screw.Our results indicate that employing classic oversizing of the same screw design is a safe choice for maintaining screw purchase in the bone after revision. In cases where oversizing with the same screw design is not practical, opting for a PUDL screw with the same original diameter can provide enough purchase in the bone to maintain stability. Due to the complex loading environments in the spine or in patients with compromised bone stock, surgeons may find it necessary to remove and replace failed pedicle screws. Removing a pedicle screw can substantially reduce the quality of the bone, and therefore limit the holding capacity of the replacement screw. The revision screw is typically larger than the original pedicle screw to improve purchase with surrounding bone. Theoretically, replacing a screw with one of larger diameter and longer shaft could increase the contact area between the bone and screw, resulting in a greater pullout strength due to the increased friction force , 2. HoweAlternatively, pedicle screws augmented with bone cements such as polymethylmethacrylate (PMMA) or calcium phosphate can also improve the fixation strength , 5. HoweRecent studies have investigated the relationship between the thread design on the screw and its pull-out strength –10. Weeg3) ) were used as a substitute for cadaveric bone because of the consistent material properties, homogenous structure, and ready availability [3 was chosen based on the results of previous literature showing similar compressive mechanical properties with vertebral cancellous bone [Rigid polyurethane foam blocks , and one prototype which was designed explicitly for use in this study by the investigators and manufactured by Bricon GmbH, were compared in an axial pullout test Fig. . All scrAn MTS MiniBionix testing system  equipped with an MTS axial/torsional load cell model 662.20 H-05) and a custom-made pulling jig was used to perform the pullout strength test according to ASTM F543  .20 H-05 The pull-test was performed by pulling the screw away from the test block at a rate of 1 mm/s. The fixation strength of each screw was defined by the maximum load recorded prior to failure by any means. Each of the four screw designs was tested five times using separate screws and separate foam blocks for each test.Test Sequence.In the first sequence, a 6.0 mm diameter screw was inserted into the predrilled foam block and the primary pullout strength of the screw was recorded Fig. A.In the second sequence, a 6.0 mm screw was inserted and removed, and then a new screw was inserted which was either (i) a 6.5 mm diameter screw of the same design, or (ii) a different screw design with a diameter of 6.0 mm. The pullout strength of the revision screw was recorded as the ‘revision pullout strength’ Fig. B.Each sequence was conducted five times for each screw design and the mean pullout strength and standard deviation were calculated.Fisher’s post hoc test with a level of significance of 0.05 was used to ascertain significant differences between individual means when the analysis of variance identified significant differences.In the first sequence, the mean pullout strength of MSLT was significantly (p < 0.05) greater than all other screw designs Table . The difThe pullout testing of pedicle screws in study was performed by applying a gradual axial force at a constant displacement to a screw inserted into a block of synthetic bone, and the maximum force required to extract the screw was recorded as the pullout strength. Although such axial loading does not replicate the actual conditions in the body, the simplicity and reproducibility of the test method makes it an efficient method of investigating a screw’s holding capacity , 16. OurIn our study, the MSLT group had the greatest pullout strength for the primary fixation, and the PUDL group had the lowest. This is likely because the MSLT screw had more threads than the other designs and the bone-screw interface is a critical factor affecting the screw purchase in bone. There was no statistical difference in the primary pullout strength of the STL and DLT groups, but the difference in the remaining groups was significant (p < 0.05). A biomechanical study by Shen et al.  indicateWhen the same size screw with a different thread was used for the revision (second sequence), the pullout strength for all revision screws was lower than when the primary screw was replaced by the same screw of larger diameter (second sequence). When an STL or MSLT screw was used for the primary insertion, inserting a PUDL screw for the revision resulted in the greatest pullout strength of all groups in the second sequence. If a DLT or PUDL was used for the primary insertion, the STL screw had the greatest pullout strength when used for the revision. The results show that the revision pullout strength in the second sequence was lowest when using a DLT or MSLT screw. This is likely because the insertion path was already tapped by the primary screw and the insertion hole was more severely damaged by the dual-thread or mixed-thread screw in the revision procedure. After inserting a DLT or MSLT screw for the revision, there was noticeable damage to the Sawbone in the MSLT group at the bone-screw contact interface Fig. , which iIncreasing the diameter of the revision screw improves the purchase in the bone, forcing the thread to engage in the bone around the existing tunnel , 10. SenThere are some limitations to this study. Firstly, the synthetic bone blocks used are designed to replicate the properties of bone, but the nonlinear properties of human bone cannot be truly represented by such synthetic materials. However, synthetic blocks are routinely used in biomechanical studies because they are readily available, are cheap and display low interspecimen variability. Second, the pullout strength of the screws was only tested under an axial force, which does not represent the loading conditions in the body or the forces placed on pedicle screws. Third, we restricted our study to four common screw types with defined diameters and excluded other pedicle screw designs. The results may not be representative of all screw types. Last, this study only considered the situation where the primary screw was removed and a secondary screw reinserted into the same hole without considering other aggravating factors that may lead to screw loosening, such as screw hole enlargement. Future work may investigate the fatigue properties of the different screw insertions under cyclic loading.The results of this study show that a classic oversizing approach with the same screw design is the best option for a revision screw. If oversizing with the same screw design could is not feasible, using a PUDL screw with same original diameter can provide adequate purchase in the bone to maintain stability.


Even though the prevalence of allergies is increasing, population‐based data are still scarce. As a read‐out for chronic inflammatory information, new methods are needed to integrate individual biological measurements and lifestyle parameters to mitigate the consequences and costs of allergic burden for society.More than 480.000 data points were collected from 1462 Luxembourg adults during the representative, cross‐sectional European Health Examination Survey, spanning health and lifestyle reports. Deep IgE‐profiles based on unsupervised clustering were correlated with data of the health survey.42.6% of the participants reported a physician‐diagnosed allergy and 44% were found to be IgE‐positive to at least one allergen or extract. The main sensitization sources were tree pollens followed by grass pollens and mites , suggesting seasonal as well as perennial burden. The youngest group of participants (25–34 years old) showed the highest burden of sensitization, with 18.2% of them having IgE to 10 or more allergen groups. Unsupervised clustering revealed that the biggest cluster of 24.4% of participants was also the one with the highest medical need, marked by their multi‐sensitization to respiratory sources.Our novel approach to analyzing large biosample datasets together with health information allows the measurement of the chronic inflammatory disease burden in the general population and led to the identification of the most vulnerable groups in need of better medical care. The European Health Examination Survey is a standardized survey to measure health and lifestyle attributes in European countries.2The following are brief descriptions of the methods used in this study. For further information and references, please see the supplementary methods.2.1N = 1529 participants; median age 45; range 26–65 years; Table Adult participants were included from a representative population‐based cohort established earlier in the frame of the European Health Examination Survey in Luxembourg  at the Luxembourg Institute of Health. For participants who did not report physician‐diagnosed allergies, sera were analyzed with the ImmunoCAP Phadiatop SX1  for sIgE‐screening to 8 respiratory allergen sources. The resulting positive samples were also subjected to IgE multiplex profiling  were tested in IgE multiplex profiling. Serum IgE‐typing was conducted with 298 allergen(s)/extracts using the Alex2 assay  and R Studio (version 2022.02.03+492). Total IgE values were excluded from the unsupervised clustering as quantification using the Alex2 assay is semi‐quantitative only (range of 20.00–2500.00 kU/L). For each established patient cluster, total IgE were then summarized. Data normalization, clustering, and Uniform Manifold Approximation and Projection visualization were performed using the Seurat package (version 4.0.6). Default settings were adjusted to accommodate the lower read depth and dimensions. Log‐normalization was performed using a scale factor of 1000 and the “FindVariableFeatures” function was used with “nfeatures” set to 300. Data scaling and principal component analysis were carried out to build a Nearest‐neighbor graph (“FindNearestNeighbor”). The function “FindCluster” with a resolution of 0.5 resulted in a total of 7 clusters.2.4Data analysis was performed in R studio. Descriptive statistics were calculated using the R base functions. The package tidyverse was used for data structuring and cleaning. Unless otherwise mentioned, graphs were visualized using ggplot2. Correlations of categorical values were calculated using Cramer's V and a correlation matrix was computed using the corrplot package. Circos plots were created using the circlize package. Spatial maps were created using data for spatial visualization of Luxembourg publicly available through a national data platform and the geojsonio package.p‐values were set to: **** <0.0001, *** <0.001, ** <0.01, * <0.05 and ns ≥ 0.05.For all significance tests, the 33.1N = 1462, Table N = 623/1462) in total, reported a clinical history of physician‐diagnosed allergy  . Allergens from food and venom sources also played a role, but to a lesser extent  and the timothy grass allergen Phl p 1 revealed the widest spread of sensitization (61.9% sensitized in the full cohort). Co‐sensitization to 7 allergens from house dust mites and 6 allergens from timothy grass pollen emerged from the deep IgE‐typing . The age group 25–34 showed 15.9% sensitization to 2 allergen groups, 9.8% sensitization to 3, 63.6% to more than 3, while in the 55 to 65‐year‐old cohort population 9.7% were sensitized against 2 allergen groups, 10.7% against 3, 30.1% against more than 3. Differing upbringing and overall living conditions may account for the 10‐times higher poly‐sensitization in the youngest age‐group   Table . To corry Figure . While Ly Figure .3.4We thought to take full advantage of integrating complex signatures of sIgE patterns. Our novel, unbiased clustering of sIgE‐profiles of 298 sIgE values per participant grouped participants into 7 clusters Figure .p < 0.0001, average 222.8 kU/L, median 98 kU/L, 20–2274 kU/L).IgE cluster 0 had two main characteristics: the highest sensitization to pathogenesis related class‐10 (PR‐10) molecules and the highest level of co‐sensitization to other main groups. Here, 24.4% of participants were gathered together. The allergens/extracts most different between this cluster and all other clusters  were found for hazelnut allergens and extracts Table  with an A/L for Ves v 5 and 0.4 kUA/L for Ves v extract (0–6.0 kUA/L for Ves v and 0–14.6 kUA/L for Ves v 5).IgE cluster 1 was defined by wasp venom sensitization, with the wasp venom extract Ves v and the concurring allergen Ves v 5 being the differentiating allergens. This cluster comprised 24% of sensitized participants. Here, the total IgE median was only 20 kU/L, which is the lower cut‐off of total IgE measurement. Mean sensitization against the wasp allergen was low, with an average 1.0 kUA/L) with lower average sIgE levels for Phl p 1, Lol p1, and Phl p 6 .IgE cluster 2 comprised 19.6% of the sensitized study population and was defined by sensitization against grass pollen allergens and extracts. The timothy grass pollen allergens Phl p 5.0101, Phl p 1, Phl p 6 and Phl p 2 were under the most differentiating allergen features in this cluster Table . Other rA/L), with lower levels of sIgE between 4.0 and 1.1 kUA/L for other mite‐allergens. 76.2% of this cluster were sensitized to half or more of the signifier allergens, suggesting a higher clinical burden.IgE cluster 3 encompassed 15.7% of the sensitized cohort, with a clear pattern of house dust mite sensitization. European/American house dust mite allergens were the most differentiating allergens for this cluster , as well as the storage mite allergens Lep d 2 and Gly d 2. The average total IgE was the second highest for all clusters at 208.6 kU/L . The main sensitizing allergens were Der p 2 and Der f 2 , while participants with sensitization to animal epithelia and dander cluster together in cluster 6 . Notably, of these IgE clusters, only participants in IgE cluster 6 have a median total IgE over the cut‐off value, suggesting a higher clinical burden in this smallest group.IgE Clusters 4, 5 and 6 grouped 6.6%, 5.8% and 3.9% of the cohort, respectively. Briefly, IgE cluster 4 comprised participants with sensitization to ash extract and allergen Fra e and Fra e 1 that had no sensitization to most differentiating allergens. This group had the lowest average total IgE . Cluster 5 encompasses sensitization to different tree pollen and food, 3.5To establish the potential health and socioeconomic burden of the above clustered participants, the health reports of participants of each IgE cluster were compared with those of the other sensitized or non‐sensitized participants on variables related to overall health and lifestyle.p < 0.0001, p < 0.0001, p = 0.04, p = 0.03 respectively, Figure P = 0.027), and even though not significant anymore after p‐value correction, a higher absence from work due to health problems (43.9% vs. 37.2%) . Another example would be participants in IgE cluster 3, the house dust mite cluster. Those participants were mostly males (p = 0.04). Indeed, an association between male sex and sensitization to D pteronyssinus and D farina had been demonstrated before.Notably, participants in the biggest cluster, IgE cluster 0, among other things reported more eye, nasal, and food allergy and asthma than others within the sensitized cohort, suggesting a high clinical burden (%) Table . This cl4To our knowledge, this is the first study using a well‐established cross‐sectional population‐based health survey together with deep IgE‐profiling  and unbiased unsupervised non‐limiting clustering to investigate connections between sensitization patterns and medical needs.Within the EHES‐LUX‐cohort, physician‐diagnosed allergy report was high, with 42.6% of the participants reporting having ever had any type of allergy. These numbers are comparable to other findings in middle and northern Europe.Participants were overwhelmingly sensitized to respiratory allergens, both seasonal and perpetual (Figure For respiratory allergens, our participants often showed sensitization to multiple molecular allergens per group suggesting high clinical reactivity, such as for grass pollen with complex IgE patterns for the allergens Phl p 1, 2, 5 and 6 Figure , we detePoly‐sensitization was especially high in the youngest age group Figure . This coOur unsupervised clustering approach revealed 7 different clusters, most of which were defined by respiratory molecular allergens Figure .Indeed, respiratory allergens appear to play an important role in the trajectories of allergic diseases from child to adulthood. Wickman et al. described such risk allergens as early predictors of respiratory disease at the age of 16 years, using two birth cohorts.In our study, the main IgE clusters 0, 1, 2, and 3 encompass 83.7% of the sensitized population and are defined by respiratory allergens/extracts and structurally similar food allergens as well as insect venoms. For IgE cluster 1, comparable levels of venom sensitization have been found in a German cohort of a similar sized population, as well as in other European studies.IgE cluster 4 was defined by sensitization to Ash tree pollen allergen and extract. Interestingly, sensitization against the main olive tree allergen Ole e 1 was not differentiating enough between clusters to be classified as a stratifying allergen, even though it belongs to the same group of Oleaceae family. This might be explained by the clustering approach considering IgE positivity as well as IgE levels and global patterns. While 28.9% of Fra e 1 sensitized participants had no Ole e 1 sensitization, Fra e 1 also elicited sIgE levels three times as high as Ole e 1. Therefore, Fra e 1 was determined as a differentiating allergen.For IgE clusters 0, 2 and 3 the average total IgE levels of the participants of each cluster as well as the average levels of sIgE for the differentiating allergens of each cluster mark these participants to be clinically reactive.Poly‐sensitization to inhalant allergens from tree or grass pollen as well as house dust mite and animal dander has been reported before, with 70% of birch pollen allergic adults in the UK also having allergic reactions against foods.Our study has several strengths and limitations. We used a respiratory prescreening IgE test to select samples for IgE array analyses, which is an approach also used by others.In our cohort, high sensitization and allergy reports, together with significant medical need, show the urgency to decrease the impact of allergies on a personal and a socioeconomic level. Unsupervised clustering of large sIgE datasets allowed the identification of a group of participants with the highest burden by their sensitization patterns. Multifaceted computation‐based methods may in future lead to a more targeted clinical supervision of patients.A recent study on 9 childhood cohorts showed sensitization in the context of regionality. IgE profiles varied depending on the geographical exposome of participants.Recent results from the Finnish allergy program show promising results in mitigating stressors leading to sensitization and also the need for country‐wide intervention.Rebecca Czolk wrote the manuscript and analyzed the data. Oliver Hunewald established the data clustering approach. Naphisabet Wanniang helped with clinical interpretation. Gwenaëlle Le Coroller provided support for the statistical analysis and accessed and verified the data. Maria Ruiz‐Castell, Michel Vaillant and Guy Fagherazzi managed the EHES‐LUX cohort. Annette Kuehn developed the study concept and supervised and coordinated the study. Christiane Hilger, Françoise Morel‐Codreanu and Markus Ollert revised the study concept and manuscript. All authors provided critical feedback on the manuscript concept and scientific content.The authors declare that they have no relevant conflicts of interest.Supporting Information S1Click here for additional data file.


For perspective, this is enough heat to melt > 1 mm/hour of ice. This finding should be compared with climate models and in reanalysis data sets to further quantify sea ice albedo’s role in Arctic Amplification. The measurements were made with previously unpublished pixelated radiometers on Global Positioning System satellites from 2014 to 2019. The GPS orbits give each radiometer instantaneous and continuous views of 37% of the Earth, two daily full views of the Arctic and Antarctic. Furthermore, the GPS constellation gives full-time full-Earth coverage that may provide data that complements existing limited field of view instruments that provide a less synoptic Earth view.New measurements from the Arctic ± 40 days around the summer solstice show reflected sunlight from north of 80°N decreases 20–35%. Arctic sea ice coverage decreases 7–9% over this same time period (as reported by the NSIDC) implying Arctic sea ice albedo decreases in addition to the sea ice receding. Similar Antarctic measurements provide a baseline to which Arctic measurements are compared. The Antarctic reflected sunlight south of 80°S is up to 30% larger than the Arctic reflectance and is symmetric around the solstice implying constant Antarctic reflectivity. Arctic reflected sunlight 20 days after solstice is > 100W/m Several theories have been developed to explain the more rapid rate of warming of the Arctic region  compared to the rest of the Earth. Likely reasons behind Arctic Amplification include4 “reduced summer albedo due to sea ice and snow cover loss, the increase of total water vapour content in the Arctic atmosphere, changes in total cloudiness in summer, additional heat generated by newly formed sea ice across more extensive open water areas in the autumn, northward transport of heat and moisture and the lower rate of heat loss to space from the Arctic relative to the subtropics”6. Climate model-based analysis drives interpretation and hypotheses of causes behind Arctic Amplification. Several analyses have found sea ice albedo feedbacks are likely driving Arctic Amplification12. The albedo feedback is due to (1) sea ice melting that leads to recession of the ice pack as well as (2) decreasing reflectivity or albedo of the remaining ice due to surface melt18 that changes the snow and ice surface reflectivity as well as forming poorly reflecting melt ponds. Most climate system analyses recognize sea ice recession as an important effect in reducing albedo20 but the role of reduced albedo23 of the remaining sea ice is less frequently mentioned. Recent analyses of comprehensive Arctic albedo measurements over enough years to be climatologically significant  appear to be few in number.The Arctic region’s fast changing climate is causing phenomena such as the decrease of the minimum (September) sea ice extent by almost 40% since the 1970s24 at an altitude of 20,200 km. The GPS pixelated radiometers have collected measurements since 2013 and are planned to continue until 2040. Data from these instruments may provide a valuable complement to NASA’s Clouds and Earth Radiant Energy System (CERES) project27 and other measurements for determining the Earth’s radiation balance29 by providing full-time, full-Earth coverage with multiple satellites viewing all points on the Earth. The CERES program has six radiometer packages, FM-1 to FM-6 on four satellites in nearly circular low altitude near-polar orbits. FM-1 and FM-2 are on the Terra satellite, FM-3 and FM-4 are on the Aqua satellite, both launched in 1997 in 705 km high orbits. FM-5 is on the S-NPP satellite launched in 2009 and FM-6 is on NOAA-20 launched in 2014 in 834 km orbits. At this writing (2023) Terra and Aqua may be nearing end-of-life. The CERES instruments each have three bolometer-based uniform spectral response radiometer channels: short wave (0.3–5.0 microns), window (8–12 microns) and total (0.3–100 microns). Moderate Resolution Imaging Spectrometer (MODIS)30 hyper-spectral imagers are on Terra and Aqua and Visible Infrared Imager-Radiometer Suite (VIIRS)31 hyper-spectral imagers are on Suomi NPP and NOAA-20. The MODIS and VIIRS spectral bands are similar to geosynchronous weather satellite imager bands so that CERES measurements can be transferred from CERES to MODIS/VIIRS to weather imagery to provide radiometric coverage geographically and temporally. CERES has frequent, but not continuous, polar coverage. Although the GPS radiometers have narrower spectral coverage, described in detail below, than the extensive CERES instrument suite, the GPS radiometers may complement CERES with (1) better full-Earth, full-time coverage for measuring reflected sunlight, particularly providing full-time coverage of the polar regions not visible from geosynchronous orbits and (2) multiple simultaneous views of all points on the Earth to sample angular reflectance variations.Earth-reflected sunlight measurements have been made by pixelated broadband silicon radiometers  operated by the U.S. Government on seven Global Positioning System (GPS) satellites32. Cloud products were produced from AVHRR data by the International Satellite Cloud Climatology Project (ISCCP)34.Earth reflected sunlight was also measured with the Advanced Very High-Resolution Radiometer (AVHRR) series of instruments on U.S. National Oceanic and Atmospheric Administration (NOAA) series of polar orbiting weather satellites from 1979 to 201937. Decrease in sea ice coverage alone is commonly identified as a contributor to Arctic amplification38, with limited attention to accompanying albedo decrease. Exceptions are Lindsay39 who reported a summertime decrease in central Arctic albedo based on AVHRR satellite measurements and Lei16 who studied the Beaufort Sea. Review of ISCCP and MODIS cloud coverage measurements shows that the reflectivity change is not due to cloud cover effects. The GPS measurements described here show that change in the albedo of the remaining sea ice must make a large additional contribution to Arctic amplification. The difference in Arctic and Antarctic albedo 20 days after the respective summer solstices results in Arctic Top of Atmosphere reflected sunlight more than 100 W/m2 smaller than Antarctic reflected sunlight, corresponding to sufficient absorbed sunlight to melt more than 1mm of sea ice per hour, if it were all absorbed by sea ice.This paper describes summertime measurements of Earth-reflected sunlight from the North and South Polar regions north and south of 80°N/S made with the GPS-based radiometers. These calibrated radiometers collect a coarse image of the full visible disk of the Earth every 12.9 min. The first application of these radiometer measurements to climate science is described here. These measurements show that the albedo of the Arctic sea ice north of 80° decreases much more rapidly, likely due to melting effects mentioned above, than the sea ice areal coverage decreases as measured using satellite-based microwave measurementsThis section presents time series measurements of sunlight reflected from the Arctic north of 80°N latitude and Antarctic south of 80°S latitude made with seven GPS Block IIF radiometers. Each radiometer pixel can be traced to an area on the Earth using the pixel number, the satellite coordinates in an Earth-Centered-Earth-Fixed (ECEF) coordinate system, and the satellite’s reported pitch, roll, and yaw angles. The satellite coordinates are determined from the satellite orbital parameter data. In this calculation, the Earth is assumed to be a perfect sphere. The pixels that project to locations north of 80°N latitude or south of 80°S latitude are selected from the datasets taken at the first southern and northern orbital extreme each day. Pixel currents are summed and converted to black body equivalent irradiance at the radiometer aperture. The most uncertain part of the process is the conversion from pixel current to black body equivalent irradiance that assumes directionally uniform (Lambertian) reflectance and uses an estimate of the spectral variations of atmospheric transmission and surface reflectance as described in the Methods section. Irradiance is back projected to Earth to determine Top of Atmosphere (ToA) reflected sunlight. The incident power on the radiometers as plotted below is an upper bound, and the uncertainty factors differ for the Arctic and Antarctic.2. As described in the Methods section, the year-to-year decrease in the SVN65 signal and the increase in the SVN68 signal appear to be due to some combination of changes in (1) the satellite longitude from year to year, which affects the view of Antarctica; and (2) the time of day of minimum latitude, which affects the solar longitude, Sun-Earth-Vehicle (SEV) angle that is the light reflection angle. The greater reflectivity of the Antarctic than the Arctic is due to the greater reflectivity of the Antarctic glacier and snow cover than of the Arctic snow-covered sea ice.The upper (lower) frames of Fig. asymmetric around the solstice, peaking 15–20 days before the solstice; (2) the peak Arctic reflectivity varies by 30% over the years and is 15% to 35% smaller than the Antarctic reflectivity, and (3) Arctic year-to-year reflectivity differences are up to 30% for any given day, particularly near 20 days after the solstice.Figure 2 compared with about 195 W/m2 from the Arctic. The difference of about 135 W/m2 is largely absorbed by the Arctic surface. Some of that absorbed power may be re-radiated at longer wavelengths than the radiometer is sensitive to, but the conversion process is very inefficient. The heat of fusion of ice is about 330 J/g or 3.3e5 J/mm/m2 (energy/thickness/area). This next rough calculation puts the heat input into perspective for sea ice melting: 135 W/m2 equals 4.9e5 J/m2/hr, sufficient to melt nearly 1.5 mm/hr of ice or about 1 m/week of medium density snow (0.16 g/cm3) were it all absorbed by ice and snow.Because the Earth’s orbit around the Sun is very nearly circular the solar light flux incident on the Arctic is virtually the same as on the Antarctic on a given date relative to the solstice date. Figure 37 with 25-km pixel resolution. This dataset “provides a long -term, consistently interpreted and calibrated record for studies of climate variability and change”40. Figure Passive microwave measurements of Arctic sea ice extent are tabulated and made available by the National Snow and Ice Data Center (NSIDC)Figure The generally parabolic shape of the irradiance curves around the solstice makes it difficult to discern the change of the average Arctic albedo as the ice melts. A method is needed to compensate for the changing solar illumination of the Arctic to convert the ToA reflected flux in Fig. The resulting Arctic relative (to the Antarctic) reflectivity histories for 2014 to 2019 are shown in Fig. 34 “VIS” cloud fraction measurements for the years 2014 to 2017 and the MODIS Aqua30 cloud fraction41 measurements on the right hand axis. Corresponding cloud reflectivities are not available. The definition of the ISCCP VIS cloudiness42 is based on a reflected radiance measured with the AVHRR 650 nm channel that is in the middle of the GPS 0.4-1.0 micron spectral response. The cloudiness “clear sky” radiances (or reflectivities) across the earth are established by determining a minimum radiance for each AVHRR measurement pixel, since clouds will only increase the reflected radiance. A pixel is determined to be cloudy at a particular time if the normalized radiance exceeds the normalized clear sky radiance by a threshold that is typically 6 percentage points. The clear sky radiance for snow, sea ice or ice will be quite large so that only very reflective clouds will be recorded in the VIS cloud product. The VIS cloud coverage shown in Fig. 43 of various infrared signals. The MODIS cloud fraction varies from values below 0.2 to more than 0.8 over periods of a few days. There is no obvious relationship between the infrared-based cloud cover fluctuations and GPS-measured VNIR reflectivity.The measured reflectivity R depends not only on the reflectivity of the sea ice, but also on the reflectivity of cloud cover. Figure SI for sea ice, RW for open water and RC for clouds and coverage fractions FSI for sea ice, FCSI for cloud-covered sea ice and FCW for cloud-covered water:A simple model for the reflectivity R shows that the reflectivity SI is near 1 so the first major term will be much larger than the second major term that we therefore neglect. The remaining first term isFigure 5 shows that FFigure entclass1pt{minimaFigure 13 near Pt. Barrow at 71°N, Perovich and Polashenski14 and with relatively large area observations by Lei16 at 74°–82° N in the Beaufort Sea. Figure 13, shows the reflectivity  of dry packed snow over cold ice was nearly 0.9 on 21 May 1979 and that it had decreased to about 0.6 on 18 June in the GPS radiometer spectral range due to surface melting and pond formation. Using Perovich’s value of 0.9 for an absolute albedo of cold snow on ice as a proxy for the Antarctic normalization, the absolute albedo in Fig. This decrease in sea ice reflectivity or albedo is generally consistent with observations of melting sea ice albedo by Grenfell and PerovichThere are uncertainties in details of this analysis having to do with spectral transmission through the atmosphere and with spectral reflectivity that are discussed in the Methods section. Our review of the effects of cloud cover suggests that cloud cover did not play an important role in the years 2014 to 2017. (The trends in 2018 to 2019 seem different from 2014 to 2017.) However, it seems unlikely that more extensive analysis would alter the major conclusion that change in sea ice albedo is a significant contributor to increased sunlight absorption in addition to sea ice recession.There are pixelated silicon photodiode-based radiometers on some of the GPS Block IIF satellites that measure Earth-reflected sunlight every 12.9 min. The radiometers view 37% of the Earth’s surface from the 20,200 km high GPS orbit. The radiometers are designed to be very stable over the 15-year GPS satellite design life. About 60% of the solar power spectrum is within the radiometer spectral bandpass  decreases during the summer is a significant finding that should be widely appreciated and deserves incorporation into model-based studies of Arctic Amplification.This section describes our method to convert “ToA Reflected Flux” in Fig. Using pseudo equations:24 at an altitude of 20,200 km in six orbital planes inclined at about 55° to the equator with an orbital period of about 11:58:02 h. There have been several blocks (versions) of satellites comprising the constellation since 1978. Beginning with Block IIA in 1990 some satellites have been equipped with silicon band radiometers. The high orbit provides each radiometer a view of 37% of the Earth and the 55° inclination gives each satellite a good view of the Arctic and Antarctic twice each day as shown in Fig. Figure The GPS satellite orbits are optimized for the main navigation mission and each point on the Earth is typically in view of 8–10 GPS satellites at all times. Each day the time of revisit is 4 min earlier than the previous day; the solar illumination of the Earth as seen from GPS changes slowly from day to day and significantly over longer periods of time. This paper presents measurements collected near the southern and northern extremes of the orbits when the orbital subpoints are close to 55° south or north. Figure Figure responsivity over the roughly 15 year lifetime of the satellites. (Responsivity is the relationship between the light power at a given wavelength striking the lens and the current that the silicon detector produces.) We estimate the absolute calibration uncertainty of the spectral responsivity shown in Fig. The radiometers are designed to be very radiation hard and temperature-stable so they are expected to have very stable Si) using the peak responsivity near 930 nm. PSi is the amount of power that would be incident if all of the light were at 930 nm; PSi < P for a solar-like spectrum that is bluer than 930 nm. Knowledge of the spectrum is required to accurately convert from PSi to actual received power P. We use a conversion factor called the Inband Power Fraction (IPF)Silicon photodiodes are essentially photon counters, so the detector current is proportional to the photon flux but not to the photon power. We convert the detector current to “Silicon-Power” (P45. The spectrum at 37°N shows the effects of molecular absorption and scattering on the spectrum. Both scattering and absorption will make somewhat larger spectral changes at the poles due to longer atmospheric path lengths, but this well-vetted readily available spectrum is a convenient approximation that gives insight into the effects on the spectrum. Spectrally dependent atmospheric transmission is the ratio of the ground spectrum to the extraterrestrial spectrum. There are no doubt variations in spectral albedo over the ~ 3.5 × 106 km2 field of view in the Arctic, but some insight into the spectral albedo is given by Grenfell13 who measured albedo for several sea ice conditions. Broadly, while fresh-snow covered ice has an albedo of about 0.9 with little spectral dependence the sea ice albedo decreases as snow and ice melt and the decrease is larger in infra-red than visible. An approximation to Grenfell’s measurement of spectral albedo of “first year ice + dusting of snow” is shown in Fig. The spectrum received by the detector is altered from the solar spectrum by the spectral dependences of atmospheric transmission from space to the ground, reflection, and transmission back to space. Figure


Chamaecrista nomame (Leguminosae) using cathepsin K inhibition activity-assisted multi-step chromatography. Standard kinetic analysis was employed to examine the mechanism of cathepsin K inhibition when an isolated inhibitor and its derivative were used. The allosteric binding of these cathepsin K inhibitors was supported by a docking study using AutoDock vina. Combinations of allosteric cathepsin K inhibitors expected to bind to different allosteric sites were examined by means of cathepsin K inhibition assay. Results: Two types of cathepsin K inhibitors were identified in the methanol extract of Chamaecrista nomame. One type consisted of cassiaoccidentalin B and torachrysone 8-β-gentiobioside, and inhibited both cathepsin K and B with similar inhibitory potential, while the other type of inhibitor consisted of pheophytin a, and inhibited cathepsin K but not cathepsin B, suggesting that pheophytin a binds to an allosteric site of cathepsin K. Kinetic analysis of inhibitory activity suggested that pheophytin a and its derivative, pheophorbide b, bind allosterically to cathepsin K. This possibility was supported by a docking study on cathepsin K. The cathepsin K inhibitory activity of pheophytin a and pheophorbide b was enhanced by combining them with the allosteric inhibitors NSC 13345 and NSC94914, which bind to other allosteric sites on cathepsin K. Conclusions: Different allosteric inhibitors that bind to different sites in combination, as shown in this study, may be useful for designing new allosteric inhibitory drugs with high specificity and few side effects.Background: Cathepsin K, which is involved in bone resorption, is a good target for treating osteoporosis, but no clinically approved medicine has been developed. Recently, allosteric inhibitors with high specificity and few side effects have been attracting attention for use in new medicines. Methods: Cathepsin K inhibitors were isolated from the methanol extract of  Cathepsins are proteases found in lysosomes; cathepsin K (Cat K), a lysosomal cysteine protease that degrades type I collagen, is involved in bone resorption , while cChamaecrista nomame (Leguminosae), which is commonly used as a herbal tea, and which is grown in China and Japan—especially Kochi Prefecture—showed inhibitory activity against Cat K. Here, we report the two types of Cat K inhibitors we identified: the first type inhibited both Cat K and Cat B, and the other type inhibited Cat K but not Cat B, and is expected to function as an allosteric inhibitor of Cat K. We examined whether a combination of allosteric inhibitors binding to different allosteric sites had a higher inhibitory effect.We screened various plant extracts for inhibitory activity against Cat K. An extract of the aerial part of Since the FDA approved the use of two allosteric inhibitors, asciminib, which inhibits Breakpoint cluster region–Abelson1 (BCR-ABL1) tyrosine kinase , and deuCombining allosteric inhibitors that bind to different allosteric sites on Cat K is suggested to be a useful tactic for the development of new allosteric inhibitors in clinical medicine.Chamaecrista nomame is shown in The procedure for the purification of Cat K inhibitors from the roasted aerial part of a benzoic acid) and NSC94914 ((2-biphenylylmethyl)malonic acid)) were obtained from the US NCI/DTP Open Chemical Repository . Chamaecrista nomame (254.85 g and 986.17 g), obtained from a local farmer in Kochi prefecture in Japan, were extracted with methanol (3.4 L and 7.9 L) for one week at room temperature. After filtration, filtrates were evaporated until dry in vacuo at 40 °C to afford methanol extracts (46.89 g and 130.05 g). The purification steps of the extracts are described in 2O. Silica gel column chromatography  and HPLC  using ODS-SP  were employed for further purification, as shown in The dried and roasted aerial parts of a and pheophytin b were prepared according to the method described in a previous report [a and Pheophorbide b were prepared by hydrolysis of pheophytin a and pheophytin b, respectively, in the presence of trifluoroacetic acid.The aerial part of spinach was purchased from a local market in Kyoto. Pheophytin s report . Pheopho® 384 well Low Volume Black Round Bottom Polystyrene NBS Microplate, Product Number 4514), and fluorescence was measured using a Power Scan 4 plate reader . Inhibition ratio was calculated using the following equation:Recombinant human procathepsin K was purchased from ENZO Life Science, Farmingdale, NY, USA and activated according to the manual. Activity of Cat K was measured in 50 mM acetate buffer, pH 5.5, 1 mM DTT, 2.5 mM EDTA, 0.1% Triton-X100, 1% DMSO, and 30 µM z-Phe-Arg-AMC  as a substrate. Prior to addition of the substrate, an inhibitor was preincubated for 10 min at room temperature with enzyme to allow the establishment of the enzyme–inhibitor complex. A substrate was then added, and after incubation for 20 min at 37 °C, the enzyme activity was measured from the increase in fluorescence at 465 nm (λex = 360 nm). Assays were performed using a 384-well microplate . The activity of Cat B was measured in 50 mM acetate buffer, pH 5.5, 1 mM DTT, 2.5 mM EDTA, 0.1% Triton-X100, 1% DMSO, and 30 µM z-Arg-Arg-AMC  as a substrate. Prior to the addition of substrate, an inhibitor was preincubated for 10 min at room temperature with enzyme to allow the establishment of the enzyme–inhibitor complex. The substrate was then added, and after incubation for 20 min at 37 °C, the enzyme activity was measured on the basis of the increase in fluorescence at 465 nm (λex = 360 nm). Assays were performed using a 384-well microplate.50 were executed by GraphPad Prism 9.5.1 .Drawing curves of inhibition and calculation of ICa and 0, 0.5, 1, 10 and 50 µM for pheophorbide b. The Lineweaver–Burk plot was used to determine the inhibition mode. The kinetic parameters were calculated by curve fitting the experimental data to the equation of non-competitive and mixed-type inhibition [For kinetic studies of Cat K, the inhibition assay was carried out as described in hibition .https://pymol.org/2/ accessed on 4 March 2020), Avogadro (Version 1.2.0) (https://avogadro.cc/ accessed on 3 June 2016), AutoDock Tools (Version 1.5.6), and AutoDock vina (Version 1.1.2) (http://vina.scripps.edu/ accessed on 17 December 2020) were downloaded from internet.PyMOL (Version 2.3.5)  and the structures were optimized using Gaussian.The protein structure file was downloaded from Protein Data Bank  and the ligand, water molecules, NODocking study was performed using AutoDock Vina ,13 withoa or pheophorbide b at 5 µM or 0.1 µM, respectively, without or in combination with 10, 20, 50 µM NSC13345 or NSC 94914, was measured in the Cat K inhibition assay as described in Inhibition of Cat K activity in the presence of pheophytin


We aimed to determine whether the plasma cystatin C is a causal risk factor for cardiovascular events, stroke, myocardial infarction (MI), and cardiovascular disease (CVD) mortality by conducting Mendelian randomization (MR) designs.Our study included 277,057 individuals free of CVDs or cancer at baseline in the UK Biobank. The genetic scores of plasma cystatin C comprising 67 single-nucleotide polymorphisms were calculated on the basis of data from a large genome-wide association study. By stratifying the genetic score, we conducted cox regression to assess the relationship between plasma cystatin C and CVDs. In this study, linear MR analysis was used to estimate the causal association between plasma cystatin C and CVDs.Observational analyses showed that plasma cystatin C concentrations were associated with the risk of CVDs  and CVD mortality . Among CVDs, plasma cystatin C were associated with stroke  and MI . Linear MR analysis did not provide evidence of a causal association between plasma cystatin C and the risk of CVDs , stroke , MI , and CVD mortality , with consistent estimates from sensitivity analyses.Observational findings indicated that higher plasma cystatin C is associated with a higher risk of CVDs; According to MR studies, there is no causal association between plasma cystatin C and the risk of CVDs and CVD mortality. It belongs to the cystatin superfamily of cysteine protease inhibitors and primarily controls the activity of extracellular proteases , 2. In cPrevious prospective studies have reported that serum cystatin C concentrations are associated with the risk of coronary heart disease (CHD), myocardial infarction (MI), heart failure, and secondary cardiovascular events . In addiAt present, the causal relationship between Cystatin C concentration and CVDs was still unclear. The findings of traditional observational studies are prone to residual confounding effects and reverse causality. In addition, some factors in the multivariate analyses, such as thyroid function, remain uncorrected . MendeliIn this large-scale prospective study using UK Biobank genetic data, we first assessed the associations between plasma cystatin C concentrations and the risk of CVDs using Cox regression in observational analysis. Next, the two-stage least-squares method was used to examine genetic evidence for the associations between plasma cystatin C concentrations and the incidence of CVDs. Finally, using genetic variants associated with plasma cystatin C previously published in meta-analyses of genome-wide association studies, instrumental variable analysis was used to assess the causal association of plasma cystatin C with the risk of CVDs.2.2.1.The UK Biobank is a data repository from a prospective cohort study that was conducted at 22 assessment centers between 2006 and 2010 , 15. It n = 79,026) and those with missing values of plasma cystatin C concentration  were excluded. For the genetic analysis, the genetic data from 277,057 unrelated individuals of European ancestry were retrieved from the UK Biobank and used in our analysis. In this study, participants with cancer and CVDs at baseline  were measured and the result is available on the UK Biobank website.The primary outcomes of our study were cardiovascular events  and CVD mortality. The secondary outcomes included MI and stroke. Information on cardiovascular events and the time at which the events occurred is based on certified death records and cumulative medical records of hospital diagnoses. All CVD events were defined with 3-digit codes according to the International Classification of Diseases 10th Revision.2.3.p < 5 × 10−8), discovered in a recent published genome-wide association analysis, for plasma cystatin C concentrations in the UK Biobank (A previous study described the genotyping process and arrays used in the UK Biobank study . Sixty-s Biobank . Supplem Biobank . The eff2.4.Baseline characteristics were described as the number (percentage) of categorical variables, mean (standard deviation) for symmetrical continuous variables, and median (interquartile range) for asymmetrical continuous variables. We used Cox proportional hazards models to estimate hazard ratios (HRs) for cardiovascular risk. The samples for plasma cystatin C concentration measurement were divided into five equal groups , each separated by one standard deviation (SD). Analyses were conducted using the following three models: (1) adjusted for age, sex, Townsend Deprivation Index (continuous), physical activity, smoking status, drinking status, annual household income , and employment (yes or no); (2) additionally adjusted for body mass index (continuous), high-density lipoprotein (HDL) cholesterol (continuous), and total cholesterol; and (3) additionally adjusted for the presence of diabetes (yes or no), hypertension (yes or no), and chronic kidney disease (yes or no).For genetic analysis, we used Cox regression models to assess the relationship between cystatin C concentrations and cardiovascular events, adjusted for age, sex, and top 10 genetic principal components . The parFor the linear MR analyses, the two-stage least-squares method was used to estimate the relationships between the genetically predicted cystatin C concentrations and cardiovascular risk. First, we performed linear regression to match the cystatin C concentration with the GRS, and then performed logistic regression models to assess the association between GRS and CVDs. Both steps were adjusted for age, sex, genotyping arrays, and the top 10 principal components.In the sensitivity analysis, potentially invalid SNPs associated with confounding factors  and/or indications of known pleiotropic effects based on selected genotype-to-phenotype catalogs  were excluded, and another set of instrumental variables  was genep values for the tests were bilateral, and p values <0.05 were considered as statistically significant.To analyze observational associations, MR analyses were performed using SAS version 9.4  and R4.1.1 . Sensitivity analyses were performed using the R package, two-sample MR2.5.UK Biobank has received ethics approval from the National Health Service National Research Ethics Service (ref 11/NW/0382). A statement confirming that all methods were carried out in accordance with relevant guidelines and regulations.3.3.1.The baseline characteristics of the UK Biobank individuals are presented in 3.2.During a mean follow-up duration of 9.94 years, 21,503 CVD events  occurred. Higher cystatin C were related to a higher risk of CVDs. The HR for each SD of plasma cystatin C was 1.14 (95% CI = 1.13–1.15) (model 1). The HRs of plasma cystatin C in groups 2, 3, 4, and 5 compared with group 1 (the lowest group) were 1.07 , 1.13 , 1.28 , and 1.74 , respectively . In the In addition, higher plasma cystatin C concentrations were associated with a higher risk of stroke. Compared with group 1, the HRs of groups 2, 3, 4, and 5 were 1.05 , 1.07 , 1.17 , and 1.50 , respectively. The hazard ratio for each SD of plasma cystatin C was 1.10  (model 3). For each SD increase in plasma cystatin C, the hazard ratio for MI was 1.08  . For eacA stratified analysis based on cystatin C–GRS were performed to assess whether plasma cystatin C is related to the risk of CVDs. There was a significant association between plasma cystatin C and cystatin C–GRS, which had an effect on the risk of total CVDs (p for interaction = 0.031) and MI (p for interaction = 0.022). The HR of the risk of CVDs associated with each SD of plasma cystatin C was 1.09 (1.06–1.13) in the lowest GRS group, 1.14 (1.11–1.19) in the medium GRS group, and 1.12 (1.08–1.16) in the highest GRS group .3.3.p = 0.31) in model 1. These associations did not change after further adjustment for other lifestyle behavioral, and biochemical factors in models 2 and 3. Our results showed that each SD increment of cystatin C–GRS was negatively associated with CVDs , stroke , MI , and CVD mortality  (model 3).To assess whether a higher cystatin C–GRS is related to a lower HR for CVDs, we investigated the association between cystatin C–GRS and CVDs using Cox proportional hazards models . For CVD3.4.p = 0.27), including stroke , MI , and CVD mortality   . The res3.5.p = 0.68; stroke: OR = 0.98, 95% CI: 0.78–1.22, p = 0.83; MI: OR = 0.95, 95% CI: 0.80–1.13, p = 0.58; CVD mortality: OR = 0.70, 95% CI: 0.64–1.36,p = 0.56). When the liberal set of 67 cystatin C-associated SNPs was used as instrumental variables, the results were consistent among the four MR methods. The MR-Egger method showed no evidence of directional pleiotropy regarding the association between cystatin C and CVDs , stroke , MI , and CVD mortality  (p > 0.05).A series of sensitivity analyses  were performed to assess the association between plasma cystatin C and CVDs . MR-PRES = 0.99) . The resThe results were similar even after further exclusion of SNPs related to confounders and potentially pleiotropic SNPs. Similar results were observed using a set of 15 plasma cystatin C-associated SNPs as instrumental variables (set 1) .4.4.1.In this large-scale prospective cohort study, observational analysis found that each increase in plasma cystatin C concentration was associated with 9% higher risks of CVD events, 14% higher risks of CVD mortality, 10% higher risks of stroke, and 8% higher risks of MI. In participants with intermediate cystatin C-GRS, elevated plasma cystatin C concentrations were associated with an increased risk of cardiovascular disease and myocardial infarction. Nevertheless, MR estimates showed no significant correlations between plasma cystatin C concentrations and cardiovascular risk. However, recently published MR studies reported controversial results on the association between plasma cystatin C concentrations and CVDs. A 16-cohort MR study showed that decreased cystatin C concentrations were strongly associated with the rs91119 allele, which explained 2.8% of the difference in observational results. However, there was no evidence of a causal relationship between cystatin C concentrations and CVD events in the study  was 2.27 (1.73–1.97) compared with the lower group (≤0.99 mg/L), and the HR for MI was 1.48 (1.08–2.02), and the HR for stroke was 1.47 1.09–1.96). Multivariate modeling adjusted for confounding factors such as age, sex, race, alcohol consumption, BMI, and hypertension . The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.YX and JZ contributed to conception and design of the study. DX and YW organized the database. HS performed the statistical analysis. JT and BZ wrote the first draft of the manuscript. LH, DX, and YW wrote sections of the manuscript. All authors contributed to the article and approved the submitted version.This study is supported by the National Natural Science Foundation of China (82173648), Innovative Talent Support Plan of the Medical and Health Technology Project in Zhejiang Province (2021422878), Internal Fund of Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences (2020YJY0212), Ningbo Clinical Research Center for Digestive System Tumors (2019A21003), Ningbo Key Support Medical Discipline (2022-F22), Ningbo Clinical Research Center for Digestive System Tumors (2019A21003), and Sanming Project of Medicine in Shenzhen (SZSM201803080).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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