Manihot esculenta Crantz) is a staple crop for ~800 million people in sub-Saharan Africa. Its production and productivity are being heavily affected by the two viral diseases: cassava brown streak disease (CBSD) and cassava mosaic disease (CMD), impacting greatly on edible root yield. CBSD is currently endemic to central, eastern and southern Africa, if not contained could spread to West Africa the largest cassava producer and consumer in the continent. Genomic selection (GS) has been implemented in Ugandan cassava breeding for accelerated development of virus resistant and high yielding clones. This study leveraged available GS training data in Uganda for pre-emptive CBSD breeding in W. Africa alongside CMD and fresh root yield (FRW). First, we tracked genetic gain through the current three cycles of GS in Uganda. The mean genomic estimated breeding values (GEBVs), indicated general progress from initial cycle zero (C0) to cycle one (C1) and cycle two (C2) for CBSD traits and yield except for CMD. Secondly, we used foliar data of both CBSD and CMD, as well as harvest root necrosis and yield data to perform cross-validation predictions. Cross-validation prediction accuracies of five GS models were tested for each of the three GS cycles and West African (WA) germplasm as a test set. In all cases, cross-validation prediction accuracies were low to moderate, ranging from -0.16 to 0.68 for CBSD traits, -0.27 to 0.57 for CMD and -0.22 to 0.41 for fresh root weight (FRW). Overall, the highest prediction accuracies were recorded in C0 for all traits tested across models and the best performing model in cross-validation was G-BLUP. Lastly, we tested the predictive ability of the Ugandan training sets to predict CBSD in W. African clones. In general, the Ugandan training sets had low prediction accuracies for all traits across models in West African germplasm, varying from -0.18 to 0.1. Based on the findings of this study, the cassava breeding program in Uganda has made progress through application of GS for most target traits, but the utility of the training population for pre-emptive breeding in WA is limiting. In this case, efforts should be devoted to sharing Ugandan germplasm that possess resistance with the W. African breeding programs for hybridization to fully enable deployment of genomic selection as a pre-emptive CBSD breeding strategy in W. Africa.Cassava ( The raising energy demand globally in face of climate change is popularizing cassava as an alternative source of renewable fuel with full potential to replace fossil fuel in the developed countries . BesidesDespite the importance of cassava as food security, especially in sub-Saharan Africa, average yields still remain low (12 t/ha) compared with yield average of 20 t/ha recorded in Asia countries like Thailand . A numbeFortunately, cassava that lagged previously in terms of genomic resources relative to cereal crops like maize, rice and wheat and legumes such as common beans, ground nuts and soya beans, has received significant funding to develop complete reference genome assembly . With thGenomic selection has been reported to offer some advantages over phenotypic selection breeding scheme: (i) genomic selection allows for more cycles of recombination per unit time than phenotypic selection, (ii) selection is solely based on estimates of marker effects without prior knowledge of the QTL and also captures variation due to loci with small effects . AnotherThrough the Next Generation Cassava Breeding project, Uganda embraced genomic selection tool for cassava improvement in early 2010s. The breeding program has so far developed three recurrent genomic selection cycles, and some of the elite material has been channeled to the variety development pipeline. Our primary traits of focus include: CMD resistance, fresh root weight, end-user root quality attributes  and CBSDhttps://www.nextgencassava.org/) to predict CBSD in West Africa using 35 clones shared from IITA, Ibadan , cycle one (C1) and cycle two (C2). Briefly, C0 population was derived from forty-nine diverse progenitors that were assembled from International Institute of Tropical Agriculture (IITA), International Center for Tropical Agriculture (CIAT) and NaCRRI. Germplasm from CIAT (Columbia) targeted improvement of quality and yield traits, while germplasm from the IITA (Tanzania), and NaCRRI (Uganda) breeding programs targeted improvement of CBSD resistance. Botanical seeds from crosses  of forty-nine progenitors were planted in a seedling nursery at Namulonge, and the sprouted seedlings were evaluated in an unreplicated seedling trial at Namulonge in 2012. A total of 466 C0 seedlings were selected visually as a training population for implementation of GS based on their CMD and CBSD resistance, and evaluated for two years (2013 and 2014) at Namulonge , Kasese (mid-western Uganda) and Ngetta (northern Uganda), using an alpha lattice design with two replications. Namulonge, Kasese and Ngetta were specifically chosen because of high viral disease pressure (cassava brown streak disease and cassava mosaic disease) and whitefly (vector) populations . The C1 All clones in the training set  were evaluated for CBSD and CMD severity, fresh root weight and harvest index. CBSD foliar severity was assessed at three (CBSD3S) and six CBSD6S) months after planting using a standard scale of 1-5; where 1 = no apparent symptoms, 2 = slight foliar chlorosis, but with no stem lesions, 3 = pronounced foliar chlorosis and mild stem lesions with no die back, 4 = severe foliar chlorosis and severe stem lesions with no die back, and 5 = defoliation, severe stem lesions and die back . Cassava−1) using a hanging weighing scale of 200 kg capacity. On the other hand, CBSDRS was recorded on all harvested roots per plot using a scale of 1-5; where 1 = no observable necrosis, 2 = ≤ 5% of root necrotic, 3 = 6 to 25% of root necrotic, 4 = 26 to 50% of root necrotic with mild root constriction, and 5 = > 50% of root necrosis with severe root constriction  and cassava brown streak root necrosis severity (CBSDRS). All the ten plants were harvested and partitioned into roots and above-ground biomass (leaves and stems). Fresh root weight (FRW) and above-ground biomass were separately weighed , Nigeria. A total of 5,000 botanical seeds were generated from bi-parental crosses involving forty-eight elite progenitors. The progenitors were selected per se based on their yielding ability and resistance to cassava mosaic disease (CMD). Accordingly, these seeds were shipped and planted in a seedling nursery at Namulonge. Out of the 5000 botanical seeds, 1980 successfully emerged, giving rise to 106 families. The 1980 seedlings were thus established in an unreplicated seedling trial during the second rains of 2018 (September/October). A total of 569 clones were selected from the seedling trial for further evaluation at the clonal stage during the 2019-2020 season, which was laid out using an augmented randomized block design at Namulonge. At the end of clonal evaluation, only 297 clones remained, as half of the clones were directly culled by CBSD. These 297 clones constituted the validation set for genomic prediction, and were assessed for CMD and CBSD severity fresh root yield as the 3rd trait evaluated, following the same procedure previously described for the training population.Leaf samples were obtained from the clonal evaluation stage of the training  and the validation (germplasm from Nigeria) populations and shipped to Intertek, Australia, for DNA extraction and genotyping. Both C0 and C1 clones were originally genotyped using genotyping by sequencing (GBS) platform with 46K single nucleotide polymorphism (SNP) chip at Genomic Diversity facility of Cornell University . Howeverprcomp function in R. The first two principal components (PC) were used to visualize population structure.We used the 21,938 SNPs to assess the population structure of the training population from Uganda  and validation population from West Africa. The SNP genotypes were coded as -1, 0, or +1. Principal component analysis (PCA) was done on scaled SNP markers using the To estimate heritability for each trait per cycle of GS population  and the WA clones, we fitted linear mixed models based on experimental design for each trial, followed by extraction of variance components using restricted maximum likelihood procedure . The varlme4 package in R, accounting for the environmental differences as well as trial evaluation year as below:For genomic prediction, we fitted a two-stage prediction model. At the first stage the raw phenotypes were merged across trials  into a single data set and fitted the linear mixed model using y represents raw phenotypic value; β represents fixed effect of the grand population mean, , study year, and location, with X being the corresponding incidence matrix linking observations to those effects. c and r represent random effects of clones with Zclone and Zrep(loc/study year) being corresponding incidence matrices for clones and replications nested in location-study year respectively. The residuals ϵ were distributed as: ranef function available in lme4 package  that were used for assessment of gains from genomic selection using the three evaluated Uganda’s GS cycles . A one-way analysis of variance was performed to test for significant differences among the means of the GEBVs for the three cycles for each trait using R  and WA clones. To do the cross-validation, the BLUPs that were extracted from the first stage analyses per trait were used as the response variable to fit a second stage prediction model for five genomic prediction models with different statistical assumptions. These models were: genomic best linear unbiased prediction (G-BLUP) , BayesiaY = 1β +Xg + ϵ , with g ~ N  and ϵ ~ , where Y represents the vector of BLUPs, β represents an overall population mean, X represents the design matrix linking observations to genomic values, g being vector of genomic estimated breeding values for each clone, and ϵ represents the vector of residuals. We assumed, g has a known covariance structure defined by the realized genomic relationship matrix K, while I representing identity matrix.To implement G-BLUP, we fitted a model: Y = 1β +Zg + ϵ , with g~N  and ϵ ~ (0 I2σg), where Y represents the vector of BLUPs, β represents an overall population mean, X represents the design matrix linking observations to genomic values, g being vector of genomic estimated breeding values for each clone, and ϵ represents the vector of residuals. We assumed, g also has a known covariance structure defined by the realized genomic relationship matrix K and I representing identity matrix. Specifically, BRR assigns a Gaussian prior with common variance to each marker effect, and applies homogeneous shrinkage to all marker effects. BL employs a double-exponential prior distribution for marker effects, which places strong shrinkage to markers with little to no effect on the trait. Bayes A applies a scaled-t prior distribution to marker effects, and places slightly less shrinkage on markers with zero effect, thereby allowing more flexibility for marker effects. Lastly, Bayes B assumes that most of the markers have zero effect on the trait, and assumes that the markers with an effect on the trait will follow a scaled-t prior distribution as in the case of Bayes A, making it relatively more stringent when compared to Bayes A. All the four Bayesian models used in this study were fitted using the BGLR function available in the R package BGLR , the clones were randomly split into five subsets (5-fold), where 4/5 of the subsets were used to train the model, while 1/5 was reserved for model validation and this was replicated 5 times. The accuracy of genomic prediction for each fold was then computed as Pearson correlation coefficient between the genomic estimated breeding values and BLUPs for each trait as a response variable.0, C1 and C2 to predict disease severity and fresh root weight in the validation population (West African population) that comprised 297 clones. Similarly, the prediction accuracy for each model was assessed using Pearson’s correlation between the GEBVs and the BLUP values per trait.Lastly, we carried out independent validation for the WA clones, the five evaluated genomic prediction models  were trained using C2 > 0.65).Plot-based heritabilities were low to intermediate Table 1.Using a boxplot, we summarized variations for genomic estimated breeding values across GS cycles for the six traits assessed, with overall genetic progress recorded for most traits except for CMD Figure 1Principal component analysis revealed a slight genetic differentiation between the Ugandan and West African cassava populations Figure 2Cross-validation prediction accuracies were performed using five models  to assess prediction accuracy of genomic selection for CBSD resistance, CMD resistance and fresh root weight within the training and validation populations. We observed modest prediction accuracies for all evaluated traits and populations Figure 3Analyses were performed using C0, C1, and C2 to assess prediction accuracy of genomic selection for CBSD resistance, CMD resistance and FRW in the West African population (297 clones) that was part of the pre-breeding populations evaluated in Uganda for CBSD resistance. We observed extremely low prediction accuracies for all traits Table 3.The challenges of rapid human population growth and climate change invariably affect agricultural productivity, and thus the need for increased genetic gains . Current2< 0.2) to high (H2 > 0.6), and were well in range with heritability estimates in literature  and validation set (West African cassava population), with a slight population structure and genetic differentiation between the two populations Figure 2Several genomic prediction models have been developed to predict trait performance under different genetic architecture and the five GP models  chosen for this study also differed in assumptions about the genetic architecture of the evaluated traits. Results revealed that models performed similarly for the most part, but there also occasions where G-BLUP was slightly superior to Bayesian models used in this study. These results were in good agreement with earlier findings from Average cross validation prediction accuracies across the three populations for CBSD and CMD resistance fresh root weight ranged between 0.26 to 0.48, and were comparable to findings by Wolfe et al. (2017); On the other hand, independent validation prediction accuracies of genomic selection were generally low, and they were lower than cross-validation prediction accuracies for CMD, CBSD traits and fresh root weight. Given that the training and validation populations were fairly genetically similar, the low prediction accuracies for independent validations could be attributed to genotype by environment interaction i.e. the training and validation populations were evaluated during different seasons. These low predictions could also be attributed to the fact that west African cassava populations were deficient in CBSD resistance alleles . No consThe C0 training set yielded the lowest prediction accuracies, with negative average accuracies for all traits across all models. The disparity between the predictive ability of the C1, C2 and the C0 training sets might be because the C1 training set was able to capture more genetic signals for CBSD foliar and root symptom expression in the West African clones than C0 training set. This phenomenon was noted by Based on the findings of this study, the breeding program in Uganda has made genetic progress through GS accelerated breeding cycles for most target traits, especially for CBSD root necrosis which is one of the must to have traits in a variety, demonstrating the worthwhile of GS for rapid population improvement and variety development. In general, low prediction accuracies were recorded from using Ugandan training set to predict traits in African clones, suggesting inadequacy of utilizing Ugandan training set, especially for CBSD pre-emptive breeding in WA. In this case, efforts should be devoted to sharing Uganda’s germplasm that possess resistance with the W. African breeding programs for hybridization to fully enable deployment of genomic selection as a pre-emptive CBSD breeding strategy in W.Ahttps://www.nextgencassava.org.Publicly available datasets were analyzed in this study. This data can be found here: OA Conceptualized the study, collected and analyzed data, and wrote the original manuscript. WE collected data and reviewed the manuscript. AU collected data and reviewed the manuscript. FM collected and analyzed the data, and reviewed the manuscript. PI collected data and reviewed the manuscript. MK collected data and reviewed the manuscript. CE acquisition of the funding and reviewed the manuscript. RK collected data and reviewed the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by Next Generation Cassava Breeding Project funded by Bill and Melinda Gates Foundation, funding granted through Cornell University (grant number OPP1048542).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. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The findings withinmake it possible to reference gold nanostarsbased on their geometric properties, similar to how a radius describesa nanosphere, rather than just the LSPR of the structure—thecurrent practice. The average tip approximation presented reducesthe complexity of nanostars in discrete dipole approximation simulations.By matching the projected area and LSPR of the modeled nanostars tosynthesized nanostars, the volume, surface area, and number of tipscan be approximated without a lengthy characterization process. Knowingthe nanoparticle geometry can determine drug carrier capacity, anapproximate number of hot spots for EM imaging, and how the particlewill interact with cells. The geometric data obtained will drive thebiological application and increase the usability of this particleclass. AuNS (AuNS) sufferfrom geometric factors of two different shapes, a spherical core andconical tips, along with a volumetric ratio between the two. Additionally,acquiring the geometry of the tip in a monodisperse solution requiresaveraging the geometric properties of the tips on one star and thenaveraging that over many stars 1. Becaus11 Through these studies, it has been shown that the resonance modesof a complex structure are based on hybridization between the resonancemode(s), or eigenstates, of the elementary parts that make up thatstructure.12 In the case of a gold nanostar,these elementary parts are the core (sphere) and tips (cones).13 While the structures of individual nanostarscan be explored and modeled, the results are not necessarily representativeof the bulk solution.14Angle-resolved TEM images have given the most accuratedepictionsof individual AuNS, at the cost of a significant amount of experimentaltime.15 A large number of tips not only can provide a high density of electricfield enhancement points but also can introduce steric hindrance,leading to fewer bound molecules. Therefore, a method of determiningthe optical properties, and the approximate number of tips, on a goldnanostar is desirable.In academia,AuNS are predominantly used either as photothermalagents due to their ability to absorb light and generate heat at longerwavelengths or for their localization of electric fields at theirtips. The latter is helpful for applications such as surface-enhancedRaman scattering and fluorescence-based sensing.17 Quantifying the approximate number of tips can give insight intothe particle’s surface area, a crucial step for understandingtheir utility as drug carriers. However, depending on the tip’sgeometry, there can be an increase in uptake into cells, degradationof cell membranes, or higher adsorption to the membrane.16 It is increasingly necessary to correlate thegeometric characteristics of these nanostructures not only to theiroptical properties but also to their cellular uptake, immune systemevasion, and cargo delivery.18Beyond photothermal therapy, fluorescence,and Raman sensing, AuNShave newfound uses as drug delivery agents, as their geometry canenhance their ability to cross biological barriers.19 A characterization mechanism is needed to determine theproperties of a bulk solution of nanostars to better realize theiruse. The purpose of this study is to reduce the complexity of modeledAuNS to determine their bulk optical and geometric properties. A simplemethod is presented to generate average geometric and optical informationabout nanostars based on UV–vis spectra and TEM images, settinga precedent to include electromagnetic models as a necessary characterizationmethod to complement experimental observables of nanostars.Although thereis great interest in finding a use for AuNS, theircommercial implementation has been limited, in large part due to theirpoor reproducibility and complex characterization.2P,of each dipole at point j is found with respect toevery dipole within the system. Once every polarization has been calculated,the extinction, absorption, and scattering efficiencies  can be found using the following equations:20k is the wavenumberof interest, k = 2π/λ where λ isthe wavelength of light in vacuo, Einc is the incident electric field, Ejinc, is the incident electric field at point j, and aeff is the effectiveradius of the shape. In x” is replacedwith “ext”, “sca”, or “abs”,as all three properties are found by dividing the cross section, Cx, by the effective area ofa circle with radius = aeff, to obtainthe efficiency Qx.Gold nanostars are constructed by generatinga point-based modelon a cubic lattice for discrete dipole approximations (DDA) usingthe core radius, tip length, tip radius, and the number of tips. Pointsare treated as dipoles, and the polarization, 4 dipoles to meet theinterdipole spacing criterion, |m|kd < 0.5. In all calculations, the largest value of |m|kd was 0.0384.In the nanostar model, the core is a sphere, and the tips are conicalprotrusions, with the length and radius measured at the point of intersectiontangent to the core. Each tip is added at a random orientation usinga Euler rotational matrix. The edges of the cone are then extendedto intersect with the core. Modeled gold nanostars vary in size butare always composed of more than 1021 The dispersion information forAu is the commonly used Johnson and Christy dielectric constants.22 Each particle is generated 30 times and exposedto just one polarization. Because tips are placed randomly aroundthe particle, this ensures that an average, “bulk” responseis considered. In all the following sections, the gold nanostars willbe described in two elementary parts: the core and the tips.Gold nanostars are run inDDSCAT, a free DDA simulation software,surrounded by an aqueous environment with refractive index (RI) =1.33.4·3H2O),silver nitrate (AgNO3), sodium citrate tribasic dehydrate,and l-ascorbic acid (AA) was purchased from Sigma-Aldrich,United Kingdom. Hydrochloric acid (37%) was acquired from VWR International,United Kingdom. The heterobifunctional polymer thiol-PEG(7500)-aminewas purchased from JenKem Technology, USA. For all synthesis and experiments,Milli-Q Type 1 ultrapure water was used.To experimentally create the gold nanostars used for the study,gold chloride trihydrate aqueous sodium citrate solution was added to the flask under vigorousstirring. After approximately 2 min, the solution appeared blue-purple,which quickly turned a deep pink-red color. The solution was leftto stir under heat for a further 10 min to ensure full completionof the seed synthesis. This solution was then cooled in an ice bath,and its volume was made back up to 100 mL with Milli-Q water. Theseeds were stable and could be stored long term at 4 °C. Twosets of Gold nanostar were grown using the seeds and growth solutionsof different Au concentrations. Growth solutions were added by firstmixing 10 mL of a 0.2 mM , or a 0.6 mM (“large”),HAuCl4·3H2O solution containing 20 μLof 1 M HCl and 600 μL of seeds. While this solution was vortexedat high speed, 60 μL of 10 mM AgNO3 followed quicklyby 100 μL of 100 mM AA. After 30 s of vortexing, the fully formednanostars were then stabilized with 100 μL of a 10 mM PEG spacer(7500 Da) containing a thiol group at one end and an amine at theother.Gold nanostars weresynthesized via a seed-mediated method. Tomake the initial seeds from which the nanostars were grown, 100 mLof a 0.25 mM aqueous HAuCl3 in a 3:1 molar ratio)to 1 mL of gold nanostar suspensions. After 10 min, 6 mL of Type 1Ultrapure water was added, making the final volume 8 mL before measurement.The absorption spectra of the seedsand nanostars were recordedusing a Cary 5000 UV–vis–NIR spectrophotometer in thewavelength range between 400 and 1200 nm. For approximate determinationof nanostar hydrodynamic radius (Rh) and uniformity, dynamic lightscattering was performed using a Malvern Zetasizer Nano ZSP. The structureand morphology of the nanostar samples were determined using a JEOLJEM2100 Plus transmission electron microscope operating at an accelerationvoltage of 200 kV. Size measurements were collected using ImageJ.Gold concentrations were determined by inductively coupled plasmaoptical emission spectroscopy (ICP-OES) using a Thermo ScientificiCAP 6000 Series ICP spectrometer. Gold nanostars were centrifuged,filtered, and then dissolved by adding 1 mL of concentrated aqua regia. At lower volumes, Qext is dominated by Qabs, but as the particlesize increases, the scattering contribution increases, similar togold nanospheres . A useful takeaway from the single graph is the abilityto compare the scattering potential between two solutions of nanostarsbased on minimal calculations.Increasing the numberof tips on a core of radius = 24 nm, from 1 to 40, causes an increasein the the LSPR S2. At lo8In comparison, the molar extinctionincreases linearly with volume,regardless of tip dimensions, using the empirical relationship suggestedexperimentally by De Puig et al. and shown in 3.3The number of tips changesthe strength of the response at the resonance while also stronglyimpacting the wavelength of the resonance. By varying the core sizeand adding identical tips, the LSPR of the structure shifts towardwavelengths of higher energy: toward the resonance of the unhybridizedelementary structures. As seen in The core radius of the nanostar will limit the accuracy of allnanostar models. The dynamic range should be considered the differencebetween the largest resonance wavelength for a given tip geometryon a core, typically 5 tips, and the saturation resonance as manytips are added. In the systems studied above, a core radius of 12nm has a dynamic range of 88 nm, while a core radius of 30 nm onlyhas a dynamic range of 20 nm.aeff. A comprehensive model to determine exactgeometric features should be based on the observable aeff but should also be validated by modeling optical properties.3.4A workflow was developedusing the limitations and findings above, predicting the average geometryof gold nanostars in two bulk solutions grown using “small”and “large” gold seeds. The projected area of 20 goldnanostars per solution is measured using TEM images. The core radiusis found by creating the largest perfect circle within the star’score. The maximum radius is that of the circle that passes throughthe three largest tips. An example of how the geometric measurementsare gathered can be seen in Nanostars are modeled within the measured parameters, withtipsat random orientations, and the average projection area is used asa metric of success. Nanostars with similar projection areas are thenmodeled in DDSCAT to determine their LSPR.Of the nanostarsmodeled, only one nanostar for each solution couldreproduce the experimental resonance wavelength, the geometry of whichis seen in aeff. Nanostars with the same projection areawere also found to have a 22% variation in volume, further suggestingthat the use of aeff alone is insufficient.Three times more growth solution was added to the “large”solution, resulting in particles that have an estimated 2.6×increase in volume over the “small” growth solution.The authors hypothesize that unreacted gold may be responsible forthe discrepancy.The results above estimate thatthe volume of the successful nanostarcandidate is 50% of that which would be expected from just measuring 4Introducinga reliable characterization method that complementsexperimental observables will lower the barriers to implementing thisincreasingly valuable particle class. The technique presented aboveoutperforms typical methods of estimating the size of nanostars usingpurely experimental techniques. Specifically, the geometric properties,including particle volume, surface area, and the number of tips ina synthesized solution, can be predicted using UV–vis, TEMimages, and the DDA method outlined above. By taking advantage ofthe unique optical properties of metallic nanoparticles, these complexstructures can be characterized more completely.


Evaluating humeral head bone profile inside biceps reflection pulley area in order to identify possible anatomical variants and any causes predisposing to tendon’s instability of the long head of the biceps.This retrospective study analyzed 326 patients, 183 males and 143 females , who underwent MRI examination between 2013 and 2019. Biceps pulley reflection area morphology of 192 right shoulders and 134 left shoulders was assessed analyzing 309 MRI and 17 MR arthrography (MRA) shoulder exams. We investigated age and gender and the frequency of morphological variants among the patient groups.Four possible morphological variants were identified: 95 with convex shape; 127 with flat shape; 77 with spiculated shape; and 12 with mixed morphology. Fifteen humeral bone profiles were not classifiable.MRI was effective in defining humeral head anatomic variants inside the biceps pulley reflection area. The most frequent variants were flat or convex types. The long head of the biceps tendon (LHBT) originates from the supraglenoid tubercle, and partly, from the glenoid labrum. Lesions affecting the LHBT are considered the most frequent causes of anterior pain and disability in the shoulder, with other less common causes including rotator cuff disease involving the subscapularis and supraspinatus tendons .The LHBT passes across the “rotator interval”  before entering the bicipital groove  , 3. In iThe diagnosis and treatment of the pulley lesion, a potential cause of shoulder pain and dysfunction, represent a challenge for the radiologist and the orthopedic surgeon. The particular anatomic course of the LHBT explains its function, since as a sliding tendon, it pulls like a mechanical belt around the humeral head. Thus, it is stressed by traction, pressure, friction, and shearing forces. Apart from its close anatomic relationships with the rotator cuff, the sulcus of bicipital groove and acromion serves as additional potential sites of impingement, which may lead to secondary degenerative changes in the tendon.To our knowledge, no study has previously evaluated the morphology of the humeral head at the region of the biceps pulley reflection  which may reflect an additional potential cause of LHBT instability.This study aimed to classify the morphological variants of biceps pulley reflection area observed during MRI shoulder examinations and to analyze the frequencies and demographic characteristics for each type of variant, in order to identify those predisposing to tendon’s instability of the long head of the biceps.We retrospectively reviewed 326 cases of MRI shoulder examinations that were performed in our university hospital between 2013 and 2019. A total of 183 male patients and 143 female patients aged between 15 and 88 years (average 51.5 years) were included and they were divided into three groups according to age: ≤ 25 years, 25–50, and ≥ 50 years. Primary patient complaints were shoulder pain with or without limitation of movement and shoulder dislocation. Exclusion criteria were marked signs of degenerative arthropathy ; bone fractures ; tumor lesions (lytic or sclerotic bone lesions) of the shoulder; and qualitatively limited examinations . Approval from the Institutional Review Board was obtained and in keeping with the policies for a retrospective review, informed consent was not required.All studies were obtained with a 1.5-T scanner  with a dedicated surface coil (Flex-M). Patients were positioned with their arms in a neutral position. The morphology of the humeral head bone profile inside biceps pulley reflection area of 192 right shoulders and 134 left shoulders was assessed by analyzing 309 non-contrast MRI and 17 MR arthrography (MRA) examinations.For non-contrast MRI, axial gradient echo T2-weighted (T2w) sequence using fast field-echo (FFE) technique, oblique coronal proton density weighted (PDw), and PDw fat-saturated turbo spin-echo (TSE) images in the oblique coronal and oblique sagittal planes were obtained.The MRA examinations were performed after intra-articular injection of approximately 15–20 ml of paramagnetic contrast  via an anterior approach using a 22-gauge spinal needle. For MRA, axial 3D T1w gradient echo sequence with fat saturation (T1 High Resolution Isotropic Volume Excitation (THRIVE)), T1-weighted (T1w) TSE, and fat-saturated T1w TSE in the oblique coronal and oblique sagittal planes were obtained. MRA examinations were retrospectively selected and included in the study since the use of intra-articular paramagnetic contrast agent does not affect the imaging evaluation of humeral head morphology. Non-contrast MRI and MRA imaging parameters are summarized in Tables Images were independently evaluated by two experienced musculoskeletal radiologists .MR images were analyzed on our picture archiving and communication system , using the sequences acquired on the axial plane , the most suitable for an objective and reproducible evaluation of the biceps pulley reflection area morphology. Cases which demonstrated humeral head position deviating from neutral were not felt to affect morphologic evaluation.The reference slice  was selected by drawing a virtual plane at the great tuberosity, just above the proximal end of the bicipital groove. After a careful evaluation of the axial sequences, this seemed to be the one in which the point of passage from intra-to extra-articular portion of LHBT could be defined with greater precision Fig. .Fig. 1AxA and A1: points beyond which the sphericity of the humeral head is outlined.B: point below which the bicipital groove starts.These landmarks were identified on the reference slice Fig. :A and A1Once the landmarks were identified, a straight line was drawn connecting the landmark A with the landmark A1 Fig. . Then, aUsing this reference system, four possible main morphological variants were identified Fig. : 1. flatCategorical variables are described as frequencies with percentages. Intra-observer and inter-observer agreements were calculated using Cohen K test, with a 95% confidence interval. The values were interpreted according to the adapted guidelines of Landis and Koch. Excellent agreement occurred when the kappa value was between 0.81 and 1.00; good agreement between 0.61 and 0.80; moderate agreement between 0.41and 0.60; fair agreement between 0.21 and 0.40; and poor agreement less than 0.20 [Of the initial 326 MR shoulder examinations, 311 fulfilled the inclusion criteria. Fifteen patients were excluded from the statistical analysis because both examiners did not consider the assessment of the morphology of the biceps reflection pulley area reproducible due to poor diagnostic quality of the MRI exam or the presence of motion artifacts.convex shape with hapeFig. , 127 casape Fig. , 77 caseape Fig. , and 12 ogy Fig. .Fig. 6Aconvex shape, 17 cases (41%) as flat shape, 9 cases (21%) as spiculated shape, and 2 cases (5%) as mixed morphology. In the age group between 25 and 50 years (81 shoulder examinations), 30 cases (37%) a bicipital reflection pulley area with convex shape, 35 cases (43%) with flat shape, and 16 cases (20%) with spiculated shape. In this group, no patients had a mixed morphology. In the age group over 50 years (188 shoulder examinations), 51 cases (47%) were classified as convex shape, 75 cases (40%) as flat shape, 52 cases (28%) as spiculated shape, and 10 cases (5%) as mixed morphology.Figure convex shape were identified, 77 cases (45%) with flat shape, 48 cases (28%) with spiculated shape, and 6 cases (3%) with mixed morphology. In the female group (139 shoulder exams), 54 cases (39%) with convex shape were identified, 50 cases (36%) with flat shape, 29 cases (21%) with spiculated shape, and 6 cases (4%) with mixed morphology. There was no statistically significant difference between shoulder morphological variants and demographic age and sex groups (p > 0.05).Figure k = 0.887; 95% CI, 0.713–0.911) and also an excellent reproducibility for both observer 1  and observer 2 .The statistical analysis showed an excellent inter-observer agreement between radiologists  as it acts as a dynamic stabilizer on the one hand but, at the same time, represents a structural weakness point of the capsule providing the outlet for the LHBT , 7. The The prevalence of biceps pulley injuries is 7%, representing a significant source of morbidity . Both trOne cause of repeated microtrauma causing pulley injury is the so-called antero-superior conflict. Gerber and Sebesta  were theThe term “hidden lesions” was described by Walch et al. , referriDegenerative lesions of the rotator interval may be due to anatomical changes that cause LHBT instability –19. LBHTA prompt diagnosis of pulley lesions is required to identify intracapsular LHBT instability before rotator cuff tear occurs, and extracapsular instability to prevent subluxation and dislocation of LHBT.In 2004, Habermeyer et al.  identifiThe instability of the LHBT can also depend on the length of the medial/lateral walls of the bicipital groove, on the opening/angles of the medial wall, on the width/depth of the bicipital groove, and on the presence of the supratubercular ridge –24. DuriSeveral limitations of our study have to be considered. First, the study design is retrospective. Second, the sample size in our study is relatively small. Third, both groups of patients, with non-contrast MRI and MR arthrography examinations, were included in the study which might lead to spectrum bias. Fourth, it was not possible to obtain arthroscopic feedback of our results.Our morphological analysis opens up to a series of questions that deserve further study. In fact, in this sense, the current work was planned to explore basic data concerning morphological variants of biceps pulley reflection; this will be followed by a further study focused on verifying whether these morphological variants may be potential predisposing factors for the micro-instability of the LHBT which could therefore play an essential role in the understanding of the kinematics of the tendon related to the lesions of the pulley. Furthermore, to our knowledge, it is unknown whether the chondral print, an indirect arthroscopic sign of LHBT instability identified by Castagna et al.  and charIn conclusion, MR of the shoulder provides high accuracy in the evaluation of the morphological variants of biceps pulley reflection area and we recommend that this analysis should be preferably assessed on axial images.


To retrospectively analyze the association of serum estradiol (E2) levels on human chorionic gonadotropin (hCG) trigger day and live birth rates (LBRs) in women undergoing fresh embryo transfer and not exhibiting polycystic ovary syndrome.Retrospective cohort study.Analysis of 13,950 patients who had fresh embryo transfer between December 2013 and December 2019. The main outcome measurement was LBRs. Multivariable regression analysis was performed to investigate associations between E2 levels on the hCG trigger day and LBRs. Stratification analysis was performed to test for effect modification in subgroups. Furthermore, a two-piecewise linear regression model was established to find nonlinear relationships.Multivariable regression analysis showed a significant association between serum E2 levels on the hCG trigger day and LBRs, adjusting for covariates . Stratification analysis showed that the LBRs were positively associated  with every 1 ng/ml increase of serum E2 on the hCG trigger day for the subgroup with low antral follicle counts on the trigger day. Specifically, a two-piecewise linear regression model showed that there was a positive association  between serum E2 and LBR for every increase of 1 ng/ml E2 when the concentration of serum E2 was lower than 2.1 ng/ml. However, there was no significant association  between E2 levels and LBRs when the concentration of E2 was higher than the 2.1ng/ml inflection point.Serum E2 levels on the hCG trigger day were segmentally connected with LBRs. Assisted reproductive technology (ART) has become an integral part of infertility treatment and the proportion of infants born after ART exeeds5% in some countries . Controlin vitro fertilization (IVF) has been studied for many years, but the results are contradictory  in both fresh and frozen embryo transfer (ET) cycles . HoweverThis was a single-center, retrospective study of women who underwent ART at the Reproductive Medicine Center of the Second Hospital of Hebei Medical University between December 2013 and December 2019. A total of 38,831 ET cycles were initially included and 13,950 patients were available after screening. The study details are shown in Exclusion criteria included: 1) no embryo transfer at the time of data collection; 2) frozen oocytes; 3) patients diagnosed with PCOS as defined by the Rotterdam diagnostic criteria of PCOS ; 4) uter2) and infertility duration (years). Basal hormone levels included follicle stimulating hormone , E2 (pg/ml), progesterone  and luteinizing hormone , measured on the second or third day of menstruation. The parameters for ovarian stimulation included the ovarian stimulation protocol, total gonadotropin dose (IU), gonadotropin duration (days), E2 level (pg/ml) on the hCG trigger day, P level (ng/ml) on the hCG trigger day, antral follicular count (AFC) and number of oocytes retrieved. Embryo parameters included fertilization method, embryo stage, and number of embryos transferred.Baseline demographic indicators included female age (years), male age (years), body mass index  and the presence of respiration or any sign of life  after separation from the mother. The main outcome of the study was the LBR per ET and the clinical pregnancy rate . Early miscarriage was defined as uterine pregnancy loss at < 12 gestational weeks, and late miscarriage was defined as uterine pregnancy loss between 12 and 28 gestational weeks. Pregnancy loss rate = pregnancy losses/all studied cycles.For the gonadotropin releasing hormone agonist (GnRH-a) protocol, the patient was given 0.1 mg/day Decapeptyl  from the mid-luteal phase. After approximately 14 days, when down-regulation standards were met  after serum endocrinology and transvaginal ultrasound examinations, gonadotropin was administered until the hCG trigger day. For the prolonged GnRH agonist protocol, the patient was given 3.75 mg triptorelin  on the day 2 of the menstrual cycle. Then 28–31 days later, gonadotrophin was injected when pituitary-ovarian suppression was confirmed and the ovarian response was monitored by vaginal ultrasound and measurement of serum hormone levels during treatment.The GnRH antagonist (GnRH-ant) protocol started administration from day 2 or 3 of menstruation, followed by daily injections of 0.25 mg Cetrotide  once the leading follicle reached 14 mm diameter and until the hCG trigger day.The hCG trigger injection was 8000–10000 IU hCG  or 250 µg recombinant human follitropin alfa . The trigger day occurred when one dominant follicle had > 18 mm diameter, or > three follicles had > 17 mm diameter combined with appropriate serum hormone levels. Patients did blood tests with an empty stomach on the hCG trigger day.At 36–37 h after the hCG trigger was administered, posterior fornix puncture and oocyte retrieval were performed under the guidance of vaginal ultrasound. After 72 h, the embryos were graded according to the number of oocytes, degree of fragmentation, oocyte symmetry, multinucleation, and degree of cell fusion. The embryos were selected for transfer according to the embryo grading criteria . If the Continuous variables were expressed as the mean ± standard deviation if they exhibited a normal distribution and were expressed as the median with quartile if they exhibited a skewed distribution. The number of cases (N) and occurrence percentage (%) were used for categorical variables. Comparison between groups was performed using ANOVA , chi-square  or Kruskal-Wallis  tests to show the difference of baseline characteristics with different pregnancy outcomes, as shown in a priori on the basis of established associations and/or plausible biological relations and tested: female age, male age, infertility duration, BMI, basal FSH, basal LH, AFC, P on the hCG trigger day, endometrium thickness on the hCG trigger day, stimulation protocol, total dose of gonadotropin and gonadotropin duration, method of fertilization and type of infertility (primary infertility or secondary infertility). Screening processes were shown in Covariates were included as potential confounders in the final models if they changed the estimates of E2 levels by more than 10% with LBRs or were significantly associated with LBRs. Clinical experience and studies published in recent years were taken into consideration. The following covariates were selected Multivariable logistic regression was used for multivariable analysis. Three models were constructed, as shown in 2), infertility duration (≤1 and >1 year), AFC , endometrium thickness on the hCG trigger day (≤7 and >7 mm), infertility type (primary fertility and secondary fertility), stimulation protocol , and method of fertilization   Stratification analysis was performed and each stratification was adjusted for all covariates except the stratification factor itself. Interaction analysis was performed for female age (≤35 and >35 years), BMI (≤24 and >24 kg/mVF&ICSI) Table 6.P < 0.05 was used for statistical significance. All statistical analyses were performed using statistical software packages R  and EmpowerStats software .Generalized additive models were used to investigate the non-linear relationship between E2 levels and LBRs. The degree of freedom was determined by the minimum GCV method, which in our study is 2.4885. A two-piecewise linear regression model was used to examine the threshold effect of the E2 level on LBR according to the smoothed plot Figure 2Of a total of 38,831 fresh/frozen ET cycles from December 2013 to December 2019, 13,950 cycles were finally included Figure 1As shown in The interaction analysis was also included in the model showing that the majority of stratification factors failed to have an interaction effect with E2 levels on LBRs Table 6.Curvilinear relationships were examined for continuous variables by utilizing the two-piecewise linear regression model. This research found a curvilinear relationship between E2 levels and LBRs after adjusting for covariates, including baseline demographic indicators, parameters for ovarian stimulation, and embryo parameters Figure 2With a two-piecewise linear regression model, our results showed that when serum E2 levels were lower than 2.1 ng/ml on the hCG trigger day, the LBR increased by about 18.8% with every 1 ng/ml increase in E2. When serum E2 levels were beyond 2.1 ng/ml on the hCG trigger day, there was no significant association between E2 levels and LBRs.Follicular fluid is composed of factors secreted by granulosa and follicular cells, and plasma permeate. This fluid provides the microenvironment for the development and differentiation of the oocyte-cumulus-corona complex., granulosa cells, and follicular cells . The devThis study found a positive relationship between LBRs and serum E2 levels on the hCG trigger day when E2 concentrations were lower than 2.1 ng/ml, which was consistent with previous findings: Foroozanfard et al. conducted a study of 128 IVF cycles and showed that pregnancy rates and E2 levels were positively correlated ; SiddharP for interaction < 0.01). The finding that AFC was closely associated with serum E2 levels on the hCG trigger day was consistent with our main result. The positive impact of E2 disappeared when a patient’s ovaries over-responded. Several studies reported similar findings: Ze et al. found that high E2 levels (> 19124 pmol/L) did not influence pregnancy rates or LBRs in 2921 patients  and anti-Müllerian hormone levels. Therefore, additional well-designed and randomized control trial studies are needed to further elucidate the association of serum E2 and LBR. In addition, this study was single-center-based and may not be applicable to other centers; more studies are needed to enhance its generalizability.Based on our research, the E2 level on the hCG trigger day appears to have a segmental relationship with the LBR. Further prospective studies are required to explore the possible mechanisms and to consolidate the association between serum E2 levels on the hCG trigger day and LBR.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the ethics committee of the Second Hospital of Hebei Medical University. The patients/participants provided their written informed consent to participate in this study.XX, AY contributed to the study design. XX, YH, NC, WW and GH performed data collection, data interpretation and data analysis. NC, WW and GH advised on the conduct and coordination of the study. XX wrote the first draft of the manuscripts. NC obtained funding. All authors contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final paper.This study was supported by Natural Science Foundation of Hebei Province (H2019206674) and Natural Science Foundation of Hebei Province  (H2019206707).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. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


To investigate the association between maternal age and severe maternal morbidity (SMM) in a Korean population. Data for cases of delivery between 2003 and 2019 were extracted from the Korean National Health Insurance Service-National Delivery Cohort. The main outcome was SMM, which was determined using the Center for Disease Control and Prevention’s algorithm. A generalized estimating equation model with a log link was performed for the relationship between SMM and maternal age adjusted for covariates. SMM occurred in 40,959/2,113,615 (1.9%) of delivery cases. Teenagers and women 35 years and older had an increased risk of SMM in both nulliparous and multiparous cases (ages 15–19: risk ratio (RR) 1.32, 95% confidence interval (CI) 1.15–1.46; ages 35–39: RR 1.24, 95% CI 1.21–1.28; ages 40–44: RR 1.57, 95% CI 1.50–1.64; and ages 45 or older: RR 2.07, 95% CI 1.75–2.44). Women aged 40 years and older had the highest rates of SMM. In singleton births as well as in nulliparous and multiparous cases, teenagers and women aged 35 years and older had a particularly high risk of SMM. Identifying and managing risk factors for SMM in these vulnerable age groups may improve maternal health outcomes. From 2009 to 2019, birth rates rapidly decreased by 13.4% for women aged 25–29 years, 5.7% for women aged 30–34 years, but increased for women aged 40 years and over in South Korea2. The socioeconomic environmental change of the development of assisted reproductive technology has enabled women to conceive at older ages. This trend of increasing births among older women is expected to last as reproductive technologies improve and become more accessible3.Despite the overall decline in birth rate over the last decade, the number of births by women age 30 years and older have increased4. Even accounting for their existing diseases, healthy women with advanced maternal age have reported increasing rates of pregnancy complication5. Therefore, both prevention and management of adverse health outcomes after pregnancy for the advanced maternal age group is needed.Older women can experience adverse maternal health outcomes due to underlying comorbid conditions, such as diabetes, hypertension, and obesity6. SMM includes both psychological and physical conditions that result from or worsen during pregnancy and may adversely influence a woman’s health status. Women who develop SMM, compared to those who do not, experience increased medical costs, extended hospital stays, and potentially long-term adverse health outcomes7. Moreover, the identification of risk factors for SMM facilitate discovery of potentially hazardous health practices and may lower mortality8. Considering the increasing birth rate in older women despite a higher possibility of pregnancy complications, understanding the association between maternal age and SMM may help identify the risk factors for maternal deaths and provide focused care for vulnerable populations after delivery.To manage adverse health outcomes after pregnancy, severe maternal morbidity (SMM) is a useful indicator for both improvement and assessment of maternal health services11. On the other hand, some studies found only a higher risk of SMM among women aged 35 years or older14, 30 years or older15, or only among women aged 30–39 years16. One study found higher SMM risk in women aged 35 years or older and lower SMM risk among women aged 18 years or younger17. Thus, despite previous studies, the association between maternal age and SMM remains unclear. In addition, many studies have been conducted on Western populations; however, little information is available in the Korean context.Previously, many studies exploring the association between maternal age and SMM have been conducted; however, the findings have been mixed. Some studies found a J-shaped association between maternal age and SMM, showing that the SMM of women aged 45 years or older had the highest risk followed by women aged 40–44 years and women in teenage years, compared to those in their mid to late 20 sTherefore, we performed a nationwide population-based cohort study of childbirth cases in South Korea. The purpose of this study was to investigate the association between maternal age and SMM. In addition, this study aimed to determine the association between age and the risk of SMM by parity and the status of multiple births.18. This data was formed by the National Health Insurance Service (NHIS), which is a single insurer covering the entire population of Korea18. The data linkage of the National Health Insurance could be made by using its unique resident registration numbers given to residents by the Korean government. The NHID uses de-identified join keys to substitute for personal identifiers to link these databases and to secure ethical clearance18. The healthcare utilization database, which is the biggest component of the NHID, is collected during the claim process of healthcare services and includes information on records of inpatient and outpatient medical usage , length of stay, treatment costs, and services received and prescription records 18. The healthcare provider database contains the types of healthcare institutions, healthcare human resources, and equipment, and covers all health care institutions in Korea18.This population-based cohort study was collected from the National Health Information Database (NHID) between 2013 and 2019, which is composed of the health care utilization database, national health screening database, sociodemographic factors, and mortality for the entire South Korean populationThis study used the NHIS delivery cohort, which was extracted using the NHID claims database. The NHIS delivery cohort included all childbirth cases in South Korean healthcare institutions. This study defined childbirth using diagnosis and procedure codes for pregnancies with maternal age between ages 13 − 50 years who had a delivery hospitalization. The study population was included for at least 280 days before childbirth through 6 weeks following childbirth between January 1, 2013, and November 19, 2019. We defined childbirth as any record of delivery hospitalization that included delivery-related diagnostic codes  and procedure codes for vaginal or cesarean delivery among pregnancies with more than 37 weeks of gestation. We excluded preterm births because the database did not have specific information regarding it, such as gestational age. Different preterm births statuses (e.g. very early preterm vs early preterm) may be associated with different risk of SMM. The total number of childbirths in 2013 to 2019 was 2,203,006. We excluded women who underwent delivery after November 19, 2019 , those who had preterm births , those with more than 42 days of length of hospital stay (n = 98), and those with no information on the childbirth healthcare institution (n = 1452). Therefore, a total of 2,113,615 childbirths between 2013 and 2018 were included in this study.This research was approved by an ethical review from the Institutional Review Board of Eulji University in Korea (No. EU21-005), and all methods were performed in accordance with relevant guidelines and regulations. The requirement for informed consent was waived by the Institutional Review Board of Eulji University in Korea because all analysis used de-identified NHIS data.19. The original SMM algorithm was 25 SMM indicators based on the 9th revision of ICD7; however, in October 2015, the United States transitioned from the ICD-9 to ICD-10 diagnoses and procedures codes. The updated SMM algorithm represents serious complications of pregnancy or childbirth, such as eclampsia or acute renal failure, or procedures for the care of serious conditions, such as blood transfusion or hysterectomy. The 21 SMM indicators were composed of 16 diagnosis codes and five procedure codes.To determine SMM, we utilized the SMM algorithms developed by the Centers for Disease Control and Prevention (CDC) and included women who had at least one of the 21 previously established ICD-10 diagnosis and procedure codes during childbirth hospitalizationMaternal age at childbirth was the main exposure variable of interest. Maternal age was divided into 5-year age groups and used as a categorical variable in this study.20, parity (nulliparous or multiparous), twin birth status (singleton and twin birth), and maternal comorbidities  using Howell’s study21. Other covariates included the type of hospitals, which were divided by the number of beds , hospital regions , and the calendar year of childbirth.Possible confounders were selected based on previous studies as follows: household income level (quintile), type of insurance , and residential area  were included as socioeconomic factors. Clinical factors included mode of delivery , prenatal care  using Kessner’s Adequacy of Prenatal Care IndexWe calculated the distribution of the general characteristics of the study population by maternal age at childbirth between 2013 and 2018. The association between maternal age and SMM during childbirth hospitalization was determined using a generalized estimating equation with a log link and robust standard errors, which estimated the adjusted risk ratio (RR) and 95% confidence intervals (CI). Moreover, this model was conducted for a stratified analysis by parity and twin birth status and tested for interactions. All statistical analyses were conducted using SAS . The level of significance was set at p < 0.05.Table Table We also performed a stratified analysis of parity and twin birth status, which confirmed an interaction between maternal age and each stratified variable on SMM. In parity, women aged 40 years and older had approximately 1.5-folds higher risk of SMM in both nulliparous and multiparous women compared to the reference group . In twin birth status, women aged 40 years and older had a 1.59- and 2.1-fold higher risk of SMM in singleton, respectively ; however, the only statistically significant association was between women aged 45 and older and the risk of SMM with multiple births   countriesThis study had several strengths. To the best of our knowledge, this is the first study to examine the association between maternal age and SMM based on a nationally representative Korean delivery cohort. Furthermore, this study encompasses a very large population and long-term study because this database includes 18 years of follow up data for all Korean women who have delivered. Second, this study provides meaningful results in that not only nulliparous but also multiparous women can affect their risk of SMM by being of advanced maternal age, especially for women aged 40 years and older. This might be of value to countries which have late childbirth trends and may inform them how to avoid preventable maternal deaths or adverse maternal health outcomes. Third, this study considered several risk factors, including socioeconomic status, obstetric status, and provision factors. However, this study does have some limitations. First, the incidence of SMM might have been underestimated. Medical practitioners could under-report the incidence of SMM in medical records to report better quality indicators of services. Second, the observational nature of our study leaves room for residual confounding and other potential sources of bias, such as maternal BMI, or alcohol or smoking habits. We could not control some risk factors that were not included in this database. In addition, parity might not have been adequately adjusted for because of the limited database. The grand multiparous group (e.g. experienced five and more childbirths) and advanced maternal age might have a correlation; however, the database only contained information on the status of nulliparous or multiparous. Therefore, we could not completely adjust for multiparity, especially grand multiparity.This study has several clinical implications. As it has been confirmed that SMM is higher in teens and older women than in women in their late 20s, additional efforts to identify and manage risk factors for SMM in vulnerable age groups are needed. In addition, though it is classified as a risk group from the age of 35 years or older, the relatively young 35–39 year-old group was identified as a group with high SMM regardless of whether it was nulliparous or not. Considering these results, it would be necessary to highlight the importance of optimal childbirth age to prevent adverse maternal health outcomes in order to ensure a healthy life after childbirth.The risk of SMM by maternal age exhibits a J-shape whereby younger and older women have the highest rates of SMM, with maternal group ages 40 years and older having the highest. Furthermore, women aged 40 years and older had a particularly high risk for SMM in nulliparous and multiparous women as well as in singleton and twin births. To improve maternal health outcomes, policymakers need more specific efforts to identify and manage risk factors for SMM in these vulnerable age groups.


Introducing defects into metal nanostructures can effectively improve the catalytic activity and selectivity towards CO2RR. This review provides the recent progress on the use of metal nanomaterials with defects towards electrochemical CO2RR and defects engineering methods. Accompanying these ideas, we introduce the structure of defects characterized by electron microscopy techniques as the characterization and analysis of defects are relatively difficult. Subsequently, we present the intrinsic mechanism of how the defects affect CO2RR performance. Finally, to promote a wide and deep study in this field, the perspectives and challenges concerning defects engineering in metal nanomaterials towards CO2RR are put forward.Electrocatalytic CO In metal nanomaterials, these four surface defects widely exist. Among them, twin boundaries could be viewed clearly in five–twinned structures, including decahedron, icosahedron and twinned nanorods ,40,41,42A step is an essential kind of surface defect in metal electrocatalysts, and it influences the surface atom coordination number, which is closely connected to the surface electronic structure and catalytic performance ,55,56. T2 adsorption−desorption could be measured.Volume defects are crystal defects in three–dimensional space, which include pores, cracks, foreign inclusions and other phases that are normally introduced during processing and fabrication steps. Porous structures, the main form of metal volume defect, are featured by a large number of pore structures inside them. The pore structure can be clearly seen in HAADF–STEM images, due to the porous sites having a darker contrast because of the fewer atoms. For example, Yang et al. reported the construction of mesoporous Cu nanoribbons by in situ electrochemical reduction of Cu–based metal organic frameworks . The HAA2 is a complex process, involving electron transfer reactions of 2–12. According to the different number of electron transfer, the generated products generally include CO, CH3OH, HCHO, CH4, HCOOH, C2H4, C2H5OH, CH3COOH and so on. In addition, as a two−electron reaction process, HER is a competitive reaction for electrocatalytic CO2RR. The CO2RR process is generally divided into three stages: the reactant CO2 molecules diffuse in the liquid phase and adsorb on the surface of catalysts at the three–phase interface. Through electron transfer and multistep protonation, different reaction intermediates are formed. Then, the intermediates are eventually reduced to products and then desorb and diffuse outward into the electrolyte. As shown in 2 molecules, the intermediates *COOH and *OCHO are formed through electron–proton coupling. For the *COOH intermediate, a proton attack occurs on the specific oxygen atom, resulting in the break of the C–O bond to produce the *CO intermediate. The conversion from *COOH to *CO is a thermodynamically downhill process. For weakly adsorbed *CO, *CO is easy to desorb from the surface, and then it forms the product CO. Catalysts, such as Zn, Au and Ag, could selectively generate *COOH intermediates and have weak adsorption for *CO, leading to the main product CO. The moderate adsorption strength of *CO is favorable for subsequent protonation to form HCHO, CH4, CH3OH or C2+ products. Taking CH4 as an example  can reach 80% [The electrocatalytic reduction of CO example , after othan 90% ,59, whilthan 90% . Moreoveeach 80% .The electronic structure has been proved to be critical to enhance the activity and durability. Defects engineering is an important strategy to significantly manipulate the surface electronic structure of electrocatalysts. The defect concentration of metal causes the change of the electronic band structure and local charge distribution of the electrocatalysts, thus finally improving their activity and selectivity. Catalytic reactions often occur on the surface or interface of metal materials, resulting in the more important role of surface defects than bulk defects.2RR [3Cu is lower than that of perfect Au3Cu and Au. However, the energy barrier for *CO desorption and *H adsorption  and thus improved the faraday efficiency (FE) for alcohols, while suppressing the formation of ethylene [Point defects display localized disruptions in crystal structure and have been widely investigated in CO2RR ,62. The 2RR . As mentsorption b is quitethylene . Chen etethylene .2RR with selective HCOOH production [2 and OCHO*, and the lower negative adsorption energy of HCOOH on lattice–dislocated Bi  and a higher barrier of hydrogen for lattice–dislocated Bi  facet, resulting in the pathway change of CO2RR. Furthermore, the catalytic CO2RR activity resulting from atoms on {100} facets, single crystal edges and twin boundary edges have been quantitatively evaluated through calculating the atom−specific activity  over Ag nanocubes and five twinned Ag nanowires, which both have {100} facets exposed, but showing different types of edges [2 to CO.Grain boundaries and twin boundaries are commonly considered to be active over COreaction . It has reaction . A DFT sferently . On the of edges . The cat2 adsorption. Moreover, a relatively high density of active sites can be endowed in these materials, which can improve the efficiency of energy conversion. Different catalytic effects can be obtained by adjusting the pore size and pore density. Porous materials can also react in severe environments without significantly reducing the porosity of the materials. It has been found that porous metals are promising catalysts for electrocatalytic CO2 reduction. Highly porous structure in Zn catalyst caused a high local pH which suppressed H2 evolution, resulting in a boosted CO selectivity [2RR process. Chen et al. [3Sn/Cu6Sn5), which showed a high efficiency of 82% for the production of formate at −1.0 V vs. reversible hydrogen electrode (RHE), while the Cu6Sn5 and Cu3Sn catalysts mainly produced H2, showing 75.3% and 57.5% at the same potential, respectively. The DFT calculation results revealed that the Cu3Sn/Cu6Sn5 was preferred for the generation of formate, as well as the experimental results. The calculated adsorption energies of HCOO* were more negative than that of *COOH, and the energy difference between HCOO* and *H was beneficial for a high selectivity of the formate.Volume defects can maximize the active sites and optimize the local pH. The catalysts of the porous materials with volume defects have a high specific surface area, which is very useful for COectivity . On the n et al.  reported2RR process can generate defects.So far, a variety of methods are capable of creating defects. In general, these methods to construct defect sites are classified into a direct synthesis strategy and postprocessing modification strategy. A direct synthesis strategy indicates constructing defects during the formation process of nanomaterials. Hydrothermal/solvothermal methods and the galvanic replacement method belong to this strategy. The postprocessing modification strategy, including chemical etching, physical etching and so on, means artificially creating defects after the formation of specific materials. Herein, we introduce typical defects engineering methods for point, line, surface and bulk volume defects, respectively. It should be mentioned that more than one kind of defects are usually formed in metal nanomaterials when defects are fabricated. Moreover, not only the synthesis process of nanomaterials, but also the catalytic COTo date, a large number of synthetic methods have been reported in the literature for the generation of point defects, among which we will discuss the following typical examples, namely galvanic replacement and solvothermal methods.2RR can be prepared as well. The galvanic replacement reaction takes advantage of the potential difference between two metals to achieve the purpose of atoms substitution, which is commonly known as the formation of one kind of metal by sacrificing another metal. In view of this, researchers have achieved the regulation of the composition of Cu–Pd by galvanic replacement reactions. By controlling the concentration of the Pd precursors, Cu–Pd products with different amounts of Pd dopants can be obtained. Among them, the Cu–Pd–0.3 (0.3% Pd) exhibited the highest FE toward alcohols generation from CO electroreduction because its d–band center is at the most suitable position, and the interaction between the two metals can better balance the adsorption of intermediate *HOCCH species and *H on the catalyst surface [2RR by this method. Li and co−workers used the galvanic replacement method to grow Bi nanosheet arrays on a Cu substrate  Although there are many research works studying the effects of defects on metal nanomaterials towards CO2RR, the rational design and control of the amount and distribution of defects are rarely reported. The corresponding defects engineering strategies need to be developed. (2) So far, a limited understanding towards the growth mechanism of defects in nanomaterials has been demonstrated. Further research concerning an in–depth understanding by in situ spectroscopic and microscopic techniques is highly desired. (3) To achieve a long–term catalytic activity, the stability of defects during the catalytic performance should be paid attention to. In all, defects in nanomaterials are so vital and fascinating that they warrant devoting more efforts to exploring the construction of metal nanocrystals with defects.In conclusion, the research on developing metal nanomaterials with defects for electrocatalytic CO


Delayed graft function (DGF) after renal transplantation is a relevant clinical problem affecting long-term organ function. The early detection of patients at risk is crucial for postoperative monitoring and treatment algorithms. In this prospective cohort study, allograft perfusion was evaluated intraoperatively in 26 kidney recipients by visual and formal perfusion assessment, duplex sonography, and quantitative microperfusion assessment using O2C spectrometry and ICG fluorescence angiography. The O2C tissue spectrometry device provides a quantitative method of microperfusion assessment that can be employed during kidney transplantation as an easy-to-use and highly sensitive alternative to ICG fluorescence angiography. Intraoperative microvascular flow and velocity in the allograft cortex after reperfusion predicted DGF with a sensitivity of 100% and a specificity of 82%. Threshold values of 57 A.U. for microvascular flow and 13 A.U. for microvascular velocity were identified by an ROC analysis. This study, therefore, confirmed that impairment of microperfusion of the allograft cortex directly after reperfusion was a key indicator for the occurrence of DGF after kidney transplantation. Our results support the combined use of intraoperative duplex sonography, for macrovascular quality control, and quantitative microperfusion assessment, such as O2C spectrometry, for individual risk stratification to guide subsequent postoperative management. Delayed graft function (DGF) after kidney transplantation is an increasing clinical problem. The rising incidence rate of DGF correlates with the introduction of expanded criteria donors (ECDs) and donation after cardiac death (DCD) in the context of organ shortage . DGF is To date, the only intraoperative quality control officially requested by procurement authorities in Europe is visual assessment of the color after reperfusion and the amount of intraoperative urine production. Doppler ultrasound is commonly added to judge macrovascular perfusion of the renal allograft ,15,16. DThe aim of this study was to evaluate different methods of intraoperative perfusion assessment for predicting DGF after kidney transplantation.n = 54). Provided the absence of exclusion criteria, namely known allergy to iodine or ICG, severe hepatic dysfunction, pregnancy, hyperthyroidism, pulmonary hypertension, and the logistical availability of both technical tools for quantitative perfusion assessment (O2C and SPY Elite) and the existence of written informed consent, 26 patients were prospectively enrolled in the study  during a time period of 2 years (October 2019–September 2021) were screened for study inclusion , organ procurement, and the transplantation procedure. Postoperative care was conducted along a standardized treatment pathway  in all pFormal evaluation of the “color after reperfusion” was performed as required by the German Organ Procurement Organization (DSO) and marked on the “Kidney Quality Form” as “homogeneous”, “marbled”, or “dark blue”.A sterile ultrasound probe , BK Medical ApS, Herlev, Denmark) was connected to the bk5000 Ultrasound System . The probe was placed directly on the surface of the graft. All anastomosis, the inflow and outflow vessels, as well as the cortex and parenchyma of the kidney were closely examined for stenosis, dissection, kinking of vessels, or lack of perfusion within all areas of the graft. The result was recorded by two experienced transplant surgeons.Cortical graft microperfusion was assessed quantitatively using the O2C device . The device has been approved as a medical device class IIa according to regulation (EU) 2017/745. Micro-Lightguide O2C combines white light tissue spectrometry  and laser Doppler flowmetry (830 nm). The probe simultaneously measures the following four parameters of microcirculation from the tissue surface: postcapillary oxygen saturation of hemoglobin , relative microvascular amount of hemoglobin ), relative microvascular blood flow  and microvascular blood flow velocity .The intraoperative measurements were conducted with a sterile LFX-29 probe (catchment volume 3 mm) that was placed directly onto the surface of the allograft. The measurements were performed, according to a standardized protocol, 5 min after reperfusion at three different sites , each for a duration of 10 s. The parameter values were immediately shown on the display of the O2C device. The measurement results from the three sites were averaged to obtain a single value to represent the kidney. The measurements were only performed once, i.e., no serial scans were performed.Subsequently, the SPY Elite System  was used for intraoperative fluorescence angiography to visualize cortical microperfusion of the graft, as described in previous studies ,18,19. TThe need for postoperative hemodialysis and the parameters of early kidney function (serum creatinine levels on postoperative days (PODs) 1–10; estimated glomerular filtration rate (eGFR) on PODs 1 and 7; cumulative diuresis in the first 24 h after transplantation; and diuresis on PODs 1, 2 and 7) were monitored. DGF was defined as the need for two or more sessions of hemodialysis postoperatively, according to Schnuelle et al. .All statistical calculations were performed using SAS statistical software, release 9.4 . Continuous variables are presented as mean values together with standard deviations or  as median values together with minimum and maximum. For categorical variables, absolute and relative frequencies (percentages) are given. The comparison of two independent groups  was performed using Fisher’s exact test or the Mann–Whitney U test, as appropriate. The CKD-EPI equation was used to estimate GFR.In order to investigate the correlation between two continuous variables of perfusion assessment, Pearson’s correlation coefficient was assessed.Furthermore, logistic regression analyses were performed in order to identify parameters potentially associated with DGF. A receiver operating characteristic (ROC) curve was generated for the O2C parameters “flow” and “velocity”. For each ROC curve, the AUC (area under the curve) was estimated together with 95% confidence intervals.p < 0.05 was considered to show a statistically significant test result.For all statistical tests, This prospective cohort study contains the assessment and associated data of 26 transplanted kidneys (22 deceased-donor kidney transplantations from donation after brain death and 4 living-donor kidney transplantations). Patient and donor characteristics are displayed in DGF occurred in five patients (19%). None of the four patients after living-donor kidney transplantation developed DGF. None of the common perioperative parameters and comorbidities were found to be significantly associated with DGF, nor was the immediate urine production after reperfusion (none vs. moderate vs. good), or the type of donation or vascularization .p = 0. 8392). The average postoperative diuresis on days 0, 1, and 2 was significantly different (p = 1.0000).The average postoperative creatinine levels of both groups were significantly different on PODs 2–10 a. There ifferent b. There p = 0.3538).Concerning the visual aspect of the graft after reperfusion , 24 grafIn all grafts, perfusion was classified as “homogenous”. No major arterial, venous, or anastomotic problems were documented.In the DGF group, surprisingly, the average value for the absolute ICG perfusion parameter Ingress was higher, but the IngressRate characterizing the quality of inflow of blood into the kidney allograft was nearly half as low as in the non-DGF group. The outflow parameters Egress and EgressRate were lower in the DGF group. However, all of the four intraoperative ICG perfusion parameters were not associated significantly to DGF .p = 0.0497), flow , and velocity  .The following O2C parameters correlated significantly positively with ICG IngressRate: SO2  . There wThe mean values for the O2C parameters “flow” c and “vep = 0.1299) for the prediction of DGF.The ROC analysis of the O2C perfusion parameter “flow” yielded an optimal cutoff value of 57.34 A.U., with a sensitivity of 100% and a specificity of 82%  for the prediction of DGF. However, these associations failed to reach statistical significance.The ROC analysis of the O2C perfusion parameter “velocity” yielded an optimal cutoff of 13.33 A.U., with a sensitivity of 100% and a specificity of 82% (AUC = 0.891, CI , The results of this prospective comparative cohort study suggest that even experienced transplant surgeons cannot predict DGF by means of intraoperative visual inspection or by duplex sonography. Quantitative perfusion assessment of microperfusion should be employed to enable an accurate individual risk stratification concerning the occurrence of DGF after kidney transplantation.Concordant with the literature, the incidence of DGF in our study was about 19%. In our study population, the common risk factors were not associated with DGF. Despite the fact that postoperative serum creatinine levels and urine outputs were significantly different in patients with a normal postoperative graft function as opposed to DGF, they vary considerably in the early postoperative period. Therefore, these parameters may not allow for the prediction of short- and long-term graft function, or the individual risk stratification of DGF. Consequently, extensive postoperative monitoring is necessary for all kidney recipients until patients at risk can be identified.As known from gastrointestinal procedures, the surgeons’ clinical judgement is of limited value in predicting postoperative outcome , presumaWe assume that quantitative intraoperative assessment of the graft’s microperfusion after reperfusion is a more precise tool for predicting postoperative early graft function because it can be interpreted as a surrogate parameter reflecting the combination of risk factors present in an individual patient , organ qFluorescence angiography with ICG is an imaging modality with a predictive ability for DGF, as supported by prospective trials ,19. The To the best of our knowledge, so far, there has not been a study comparing different modalities of intraoperative perfusion assessment during kidney transplantation with regard to a patient-specific prediction of DGF. In the present study, the ICG IngressRate correlated significantly with the O2C parameters flow and velocity. Consequently, these parameters had significantly lower values in the DGF group. We were able to deliver threshold values for the parameters flow and velocity, allowing for the prediction of DGF. The results of intraoperative risk stratification via O2C can be used to guide postoperative care. Patients with a flow exceeding 57 A.U. or a velocity exceeding 13 A.U. can be transferred to normal wards earlier, conserving valuable IMC/ICU space without endangering patient safety. On the other hand, kidney recipients with a critical microvascular flow or velocity should be monitored more closely, potentially on IMC units with a focus on systemic hemodynamics, renal blood flow, sufficient hydration, and early discussion of a percutaneous biopsy to rule out acute rejection, since IRI can trigger immunologic responses predisposing to acute graft rejection ,44.The O2C method is limited by the selective measurements and the depths of penetration. This seems to apply in particular to the parameters SO2 and rHb, derived from the tissue surface by white light spectroscopy. In case of a subcapsular hematoma or an abundance of fatty tissue, the measurements of superficial parameters might fail. Therefore, we still routinely combine microperfusion measurement with duplex sonography. Even in this comparably small cohort, the O2C method showed promising results.This pilot trial delivers a solid basis for larger prospective trials with the aim of validating O2C threshold values and investigating the optimal point in time for employing intraoperative quantitative microperfusion assessment of the renal allograft for individual risk stratification concerning postoperative transplant function in order to guide postoperative management after kidney transplantation. O2C probe specifications should be reported exactly in further publications in order to allow for a comparison between different studies.


Sagittaria sagittifolia L. slices by affecting polysaccharides in the cell wall. Distilled water immersion and 0.6% acetic acid immersion (the solid/liquid ratio is 1:10) for 15 h at room temperature could result in the conversion of pectin through different reactions during thermal processing. Combined in situ and in vitro analysis demonstrated that acetic acid pretreatment could promote the interaction of cellulose microfiber or hemicellulose with RG-Ⅰ side chains during thermal processing of S. sagittifolia L. slices, promote the entanglement between linear pectin molecules and make hemicellulose show a lower molecular weight under cooking, making it easy to firmly bind to pectin, which resulted in texture changes. The findings may help improve the texture of thermally processed vegetables and fruits and deep processing of starchy vegetables.This study investigated the mechanism for acetic acid pretreatment to improve cell wall integrity and thereby enhance the hardness of cooked  Sagittaria sagittifolia L. (arrowhead) is a perennial herb belonging to the Alismataceae family. It is rich in nutrients, such as polysaccharide, protein, fat, vitamins and some mineral substances. Moreover, arrowhead bulbs also show medical value because of their antitumor, antioxidant and antibacterial activity [activity . Arrowheactivity . HoweverTexture is an important factor influencing sensory attributes of fruits and vegetables. It undergoes quick changes during thermal processing owing to cell wall disassembly ,4,5. AceCurrently, how thermal processing affects the polysaccharide structure of the cell wall in food matrices has been investigated in vitro, but the possible destruction of pectin structure by the extraction process has not been considered, which can easily affect the accuracy of the results. Some monoclonal antibodies (mAbs), such as LM 20, LM 26, LM 6-M, can be used as a very powerful tool to provide information about defined pectin domains in the cell wall and the potential interactions of polysaccharides ,15,16. BIn this research, the influence of acetic acid immersion on the texture, microstructure and cell wall polysaccharides of arrowhead bulb slices during cooking was analyzed and compared by combining immunolabeling and physicochemical analysis of isolated cell wall material from arrowhead bulb slices, so as to identify the variations of cell wall polysaccharides and their relationship with the cooked texture of arrowhead bulb slices. Our results may provide a theoretical foundation for improving thermally processed starchy vegetables and fruits in terms of quality.v/v) for 15 h (AAI). Then, 100 g slices from the DWI or AAI group with 130 mL distilled water were vacuum-packed in Nylon pouches (24 cm × 17 cm) and placed in boiling water for 0.5 h, followed by rapid cooling to room temperature with running water, which were named as cooked DWI (DWIC) and cooked AAI (AAIC), respectively. Some of the samples were used for texture measurement, SEM observation and immunolabeling analysis, and the remaining slices were kept at −80 °C for further experiments. The monosaccharide standards, namely galacturonic acid , rhamnose (Rha), galactose , mannose (Man), arabinose (Ara), glucose (Glc), fucose (Fuc) and xylose (Xyl), were purchased from Yuanye Bio-Technology Co., Ltd. . All chemicals used were of analytical grade and purchased from reliable reagent dealers.Arrowhead bulbs were obtained from a local supplier in November 2021 in Guangxi, China. Following washing with running water, the bulbs were peeled, and then made into 5 mm slices for subsequent experiments. The slices were randomly divided into two groups: (1) 200 g slices pretreated immersion in 2000 mL of distilled water for 15 h (DWI), and (2) 200 g slices pretreated immersion in 2000 mL of 0.6% acetic acid  equipped with a P/6 probe, following the descriptions of Zhao et al. [2 and gold-coated with gold sputter. Finally, sample observation was performed with SEM at an electron acceleration voltage of 5 kV under high vacuum [SEM  was employed to visualize the shape and surface morphology of the cell wall of arrowhead bulb slices under different treatment conditions. Arrowhead bulb slices were further cut into smaller pieces and fixed in 2.5% glutaraldehyde. Then, the pieces were treated with ethanol in a sequence of concentrations  after washing three times (5 min for each washing) with 0.1 mol/L phosphate buffer (pH 7.2). The samples were then subjected to critical point-drying with liquid COThe mAbs can complement the deficiency of the in vitro pectin characterization method through in situ visualization of pectin by antipectin mAbs . Pectin v/v) was supplemented to the residue, which was stirred for 20 min in 85 °C water to remove soluble sugars, and the mixed solution at a methanol–chloroform volume ratio of 1: 1 was used to remove the lipid. Finally, alcohol-insoluble residue (AIR) was obtained.The collected materials were freeze-dried and processed into powder using a blender , and then starch was removed with alpha-amylase and amyloglucosidase . About 1Following the procedure of Zhao et al. , cell waw/w) and pressing into pellets. Then, they were scanned over 4000–500 cm−1 at a 4 cm−1 resolution 64 times against KBr as the background. The data were analyzed with the Omnic 8.2 software .An FT-IR spectrometer  with KBr discs was used to collect the infrared spectra of AIR. According to Zhang et al. , samplesThe monosaccharide composition was measured with a previously described method by Liu et al. . Cell wav/v). The flow rate was 1 mL/min; the injection volume was 10 μL; and the detection wavelength was 245 nm.The HPLC analysis conditions included a C18 column (Agilent ZORBAX Eclipse Plus C18). This system used 0.1 mol/L sodium phosphate buffer solution (pH 7.2) and acetonitrile as mobile phases, and the a:b ratio was 83:17 . Pectin solutions (0.3 mg/mL) dissolved with eluent were filtrated through 0.45 μm membranes before use. A dn/dc value of 0.147 mL/g was employed for the molar mass analysis of cell wall fractions. Standard dextran with 40 kDa molecular mass was used for normalization.By using the previously described method by Zhang et al. , the polThermogravimetry  was used for thermal analysis according to Zhang and Wang  with somp < 0.05. All figures were constructed by Origin 2017 .All data were presented as mean ± standard deviation, and analyzed with SPSS Statistics 25 for Windows . One-way analysis of variance (ANOVA) with Duncan’s test was performed to compare the means. Differences were considered to be significant at The texture of plant-based foods plays an important role in their processing properties, in which hardness is one of the most important features considered by the consumers . Figure The thermally processed texture of vegetables and fruits is affected by cell wall integrity, cellular turgor and other factors, and cellular turgor disappears quickly after thermal processing . TherefoLM 20, with strong binding ability to methylesterified pectin, was used to label the slice samples. The polygonal cell wall outline was better preserved in fresh slice samples, but disappeared in cooked samples. LM 20 epitopes were identified in cell junctions, and were present at the corners of intercellular spaces in high abundance in fresh samples. However, LM 20 epitopes were only identified in cell junctions in cooked samples, which is similar to the results reported by Christiaens et al. . GeneralRedgwell et al.  demonstrLM 26 labeled branched 1, 4-galactan (one part of RG-I) and was observed in the cell wall with weak fluorescence. DWI had a higher labeling intensity than AAI, which was in agreement with the monosaccharide composition . In addi−1 are attributed to the absorption of esterified carboxyl (-COOR) and ionized carboxyl groups (-COO−), respectively. The ratio of the peak area at 1734 cm−1 to the sum at 1734 and 1650 cm−1 could be utilized to quantify the methyl esterification degree (DM) of pectin [−1 disappears in cooked samples, which is contradictory to the CLSM. This result may be due to the ratio of esterified pectin and AIR. The peak intensity at 1650 cm−1 was increased in cooked samples, indicating the occurrence of demethylation during cooking [−1 is assigned to the stretching vibration of hydroxyl groups, and the weak absorption peak at 2925 cm−1 could be ascribed to the C-H resonance of methyl groups in the sugar ring.FT-IR spectroscopy can effectively demonstrate the atom or functional group vibration in polysaccharides . It was f pectin . The pea cooking . The bro−1. The peak at 1110 cm−1 was observed as a typical absorption band of polygalacturonic acid. The AAI and DWI FT-IR spectrum exhibits a stronger peak with a maximum at around 1110 cm−1, but AAIC and DWIC show lower absorption at 1110 cm−1, indicating degradation of polygalacturonic acid during cooking. The peaks at 1162, 1050 and 1434 cm−1 were observed in all samples, which could be assigned to the skeletal vibration of a C-O-C pyranose ring and -CH2 scissoring of cellulose, respectively [Differences between AIR were also observed in the fingerprint region of 1200–950 cmectively . There wThe sugar composition and sugar ratio were analyzed for polysaccharides to compare the main chain HG and RG-Ⅰ side chain to further analyze polysaccharide structure changes in the cell wall under different treatments. WSF was loosely attached to the cell wall material . CSF is In these fractions, the Ara and Gal contents of AAIC  were significantly increased relative to those of DWIC , indicating that acetic acid pretreatment could result in the degradation of RG-Ⅰ side chains by acid hydrolysis during cooking and the transformation of CSF and NSF into soluble pectin (WSF) ,39. The The CSF in DWIC had more side chains and fewer main chains, which could hardly contribute to the formation of calcium bridges as a spatial barrier and thus decrease the sample hardness after cooking. In fresh and cooked samples, the content of Glc under acetic acid treatment was significantly increased relative to that under distilled water treatment, implying that acetic acid immersion promotes the interaction between CSF and cellulose or hemicellulose. A similar phenomenon has been found in other studies .Interestingly, HF was composed of Glc and Xyl. The characteristic sugars of pectin, including GalA, Ara and Gal, in HF suggested the co-extraction of the “hairy region”, especially RG-I, with hemicellulose fractions . TherefoPectin matrix is critical in maintaining the cell wall internal structure. Therefore, variations in pectin polysaccharides after different treatments were determined by gel permeation chromatography. Molecular weight (Mw) distribution obtained for fractions from different treatment samples is presented in max) decreased after cooking. The AIR in DWIC had no obvious characteristic pectin peak, but that in AAIC had a characteristic pectin peak at 270 °C, indicating that the structure was seriously destroyed in DWIC, and the thermal stability of AIR was improved in AAIC.Thermal analysis is important for evaluating thermal parameters of polymers. In the present study, derivative thermogravimetry (DTG) signals were used to explore the thermal degradation of different fractions. The TG curve represents the weight loss (Δm) during heating, and the DTG curve is represented by the first derivation of the TG curve and is associated with the degradation velocity . As showFor WSF and NSF, the first degradation peak between 70 °C and 150 °C can be ascribed to water evaporation. In addition, the DTG curve showed two exothermic degradation peaks at around 250 °C and 330 °C, which could be associated with the degradation of pectin and the cellulose network, respectively . The samThis research demonstrates that pretreatment with 0.6% acetic acid solution can significantly alleviate the softening of arrowhead bulb slices after cooking and maintain the structural integrity of the cell wall compared with distilled water pretreatment. The softening of arrowhead bulb slices is mainly due to the solubilization and conversion of pectin. Acetic acid pretreatment promotes the entanglement between the linear pectin molecules and interaction between RG-Ⅰ side chains and cellulose microfibers. This study provides a new approach for alleviating the softening of plant tissues under thermal processing, and reveals the precise structure of cell wall pectin in arrowhead bulb. In future studies, the relationship between the branched chain of RG-Ⅰ and cell wall needs to be further explored.