The aim of the work was to compare the interactions of three newly synthesized non-toxic starch derivatives, with varied anionic and non-ionic functional groups with methylene blue (MB) as a model cationic drug, and selection of starch derivative with highest affinity to the MB.via acetylation (SM1), esterification and crosslinking (SM2) and crosslinking (SM3), was evaluated in MB adsorption studies and assessed by FTIR, PXRD, and DSC.The native potato starch (SN), modified via electrostatic attraction between the heterogeneous adsorbent surface and the adsorbate, as demonstrated by the Freundlich plot. The FTIR confirmed vibrations assigned to N=C stretching bonds at 1600 cm-1 in the case of MB adsorbed on the SN and SM2. The most intense PXRD peaks belonged to SN and the least to SM2. In the DSC study, the thermal stability via ΔT was assessed, with SM2 of lowest ΔT value (179.8 °C).The adsorption of MB on SM2 and SM3 matched the BET isotherm model, which confirmed physisorption on the low-porous surface. In the case of SM1, adsorption took place - groups of SM2 and N+ of MB was revealed. The most amorphous structure was shown for SM2, which was correlated with the lowest thermal stability provided by the DSC study.SM2 presented the best adsorption capacity, followed by SM3 and the weakest SM1. The interactions were confirmed in the adsorption studies and may reflect applications of the modified starches as drug carriers. In the FTIR study, a probable interaction between the OH They aret al. , among oet al.  and propet al. ,21 and set al. . The figvia ionic bonds between the ionized amine group of chlorpromazine and anionic group of cyclodextrin sulfate [Some drugs have a 1,4-thiazine ring, which is included in the structure of methylene blue (MB). The study that used the similarity between MB and other molecules was performed on acridine and phenothiazine derivatives for anti-prion therapy . Selecte sulfate . Another sulfate . Therefo sulfate ,31, and  sulfate . In summ sulfate , we decivia analysis of interactions of the starches, modified by varied acidic and non-ionic functional groups, with methylene blue, as a model cationic drug. The main question was the selection of the starch derivative with the highest affinity to the MB molecule. The possible interactions were evaluated in the terms of spectroscopic structural studies, as well as thermal assessments. The interactions, which may lead to drug release prolongation, were practically confirmed in the adsorption studies, which may reflect use of the above-mentioned modified starches as drug carriers for topical application, with retardant and prolonging effect against drug release process.The aim of the work was the evaluation of the prospective pharmaceutical applicability of three newly synthesized non-toxic starch derivatives, assessed The following materials were employed in the study: methylene blue  - a model active pharmaceutical ingredient (API), native potato starch (SN) , modified potato starches  prepared according to below-described methods.The thermal and chemical modifications of native potato starch were conducted and resulted in acetylation (SM1), esterification and crosslinking (SM2), and crosslinking (SM3).D [3 /100 g of starch. After drying the acetylated starch, an initial gelatinization temperature of 49.17 °C was determined by differential scanning calorimeter Flash DSC . The acetylated starch has been stirred for 24 h at 48 °C. It was then washed three times with five-liter portions of distilled water and separated from the slurry using the Contifuge Stratos  flow centrifuge. The obtained starch was dried for 24 h in an air dryer at 30 °C.The fractions of starch particles with an average volume moment diameter D ,4 equal Native potato starch (SN) was esterified with citric acid. 10 g of citric acid per 100 g of starch dry matter was dissolved in 90 mL of water and thoroughly mixed. The resulting starch paste was left for 12 h at room temperature and then dried in an air dryer  at 50 °C for 12 h. The starches were calcined for 3 h at 100 °C. The sample was rinsed three times with ethyl alcohol with a concentration of 95 mL of ethanol per 100 mL of solution, and each time, the solution was poured over the sediment. The washed precipitate was dried in an air dryer at 30 °C for 12 h. The obtained citrate starch was hardened at a temperature 2 degrees below the gelatinization temperature (determined by DSC), dried in an air dryer at 30 °C, ground in a laboratory mill and passed through a sieve with a mesh size of 400 μm .Native potato starch (SN) (94.6 wt.%), sodium trimetaphosphate (1.2 wt.%), sodium carbonate (2.1 wt.%) and sodium chloride (2.1 wt.%) were introduced into the reaction vessel, 500 mL of distilled water at 45 °C was added. The mixture was adjusted to pH 10.5 with a 3 % NaOH solution. It was kept at 45 °C for 30 minutes with constant stirring, then neutralized with 8 % HCl to pH 6.5-6.8. The cross-linked starch was washed several times with distilled water on a funnel under vacuum and dried for 48 h at 25 °C. After obtaining starch diphosphate, it was hardened for 24 h at 2 degrees below the gelatinization temperature, dried in an air dryer at 30 °C, milled and sieved through a sieve with a mesh size of 200 μm.Based on the recipe for preparing individual modified starches, the mass values of substrates attached to native starch (SN), such as acetic anhydride, trimethaphosphate and citric acid, were converted to moles. The converted values per gram of SN are given in -1 at 32 scans per sample and a resolution of 4 cm-1.Fourier-transform infrared spectroscopy (FTIR) and attenuated total reflectance (ATR) appetizer  with OMNIC ™ software were used to determine possible interactions between MB particles and starch. The formulations of MB adsorbed on every 50 mg of starch  were dried at 40 °C and compared with a physical mixture of MB and starch in a weight ratio of 1:10 and with the standards of the pure substances. The spectra of powders were recorded at wavelengths of 400 to 4000 cmThe analysis of starch was supplemented by X-ray diffraction measurements (PXRD). The powder PXRD data were recorded on a Bruker D2 PHASER diffractometer  with a Lynxeye detector using Cu Kα radiation (0.15418 nm). All samples were measured at 295 K with 3.0 mm slit and 1.0 mm shutter. Diffractograms were obtained between 7.5° and 40° (2) (step size of 0.02° (2) and 0.25 s per step). The X-ray generator operated at 30 kV and 10 mA. The PXRD patterns were processed using the software Diffrac.Eva V 3.2. (Bruker AXS). The percentage of crystallinity and amorphousness of the tested starch samples in relation to SN was determined.-1. The thermograms were recorded at a constant heating rate of 5 °C min-1 in the temperature range from 0 to 350 °C.Differential scanning calorimetry  was performed to investigate the samples of dried adsorbed MB on 50 mg starches , their physical mixtures of 1:10 w/w and pure ingredients. The grated samples of 3 to 5 mg were explored in aluminium pans with lids under a nitrogen atmosphere, with a flow rate of 50 mL min-1 MB was added for each flask. The SN, SM1, SM2 and SM3 starch probes of 2, 5, 10, 25, 50, 125, 250, 500, 750 and 1000 mg were placed in the flasks during the series, respectively. The flasks were put on the orbital shaker (at 100 rpm) at 22±0.5 °C. Four measurements of absorbance were proceeded based on the pharmacopoeial method by sampling the MB solution volumes of 3 mL in every 5, 10, 15, and 20 minutes, and than in equal periods up to 4 h. The solution was returned to the flasks. Analysis of taken samples was done by the spectrophotometer UV/VIS Jasco V-530  at 663 nm, according to the available bibliography [-1 was prepared. The results were examined according to pseudo-zero-order kinetics and first-order kinetics. ANOVA statistical test was performed for independent groups at the significance level α = 0.05. Freundlich, Langmuir and BET isotherm models were taken for analysis of adsorption processes (kBET(s) applies to the equilibrium constant of adsorption of the first layer and kBET(L) refers to the equilibrium constant of adsorption of upper layers.The absorbance study was performed using four 50 mL conical flasks  as a series. 40 mL 6 mg Liography , and comrocesses . BET isoq and tq were calculated by the equations ::q and qtC0 is initial concentration of the solution (mg L-1), tC is concentration of the solution in time t (mg L-1), V is volume of solution (L) and m is adsorbent mass (g).where Equation  was usedm0 - the initial mass of the adsorbate, tm - is the adsorbate mass after time t.where α = 0.05 was performed for these measurements.The effect of MB adsorption on starch pH was investigated. The suspensions of pure starch in distilled water and suspensions of starch with adsorbed MB were prepared according to adsorption conditions (50 mg starch per 40 mL of water). pH measurements were taken after 1 and 72 h at 22±2 °C. Each measurement was taken three times. An ANOVA test with The results of the starch synthesis procedures yielded products with structures that conformed to the patterns  mostly followed the plot of the spectrum of the respective starch in its pure state (green line). However, there were locations where individual peaks were in excess. The peaks assigned to N=C stretching bonds at about 1600 cmt extent  a,c. Thetent .DSC analysis results shown in The results of DSC testing of native and modified starches under two heating cycles are shown in Tg, the difference in the heat capacity between the transition from glass to the liquid state ΔCp and ΔT - difference between onset crystallization temperature and Tg were analyzed using Proteus 7.0 software . The results are shown in the The glass transition midpoint temperatures -1 for SN and 22.86 mg g-1 for SM2  and the highest for SM3 (55.9 °C) (Table S1). Hydrogen bonds are among the factors affecting Tg [et al. [Tg of sucrose compared to other disaccharides. This was due to the less extensive hydrogen bonding network in the amorphous state. Similarly, Zhou et al. [Tg on hydrogen bonds occurring between polymer and copolymer particles . A network of hydrogen bonds of carboxyl groups resulted in a higher Tg value than hydrogen bonds between hydroxyl groups. The lowest Tg occurred in the absence of hydrogen bonds between polymer molecules. In our case, the holders of the hydroxyl groups were SN and SM1 (Tg 52.2 and 51.15 °C respectively) (Table S1) and the carboxyl groups were present in the SM2 molecule. In addition, phosphate groups appeared in the SM3 molecule. High Tg values for SM3 may suggest the self-association of these starch molecules, whose Tg decreased to 53.0 °C after bond formation with MB [Tg values increased after MB adsorption (54.7 °C), what may indicate low self-association of SN particles in the pure state.Glass transition temperature cting Tg . Taylor  for SM3 .9 °C , demonstrating the highest thermal stability of this starch. At the same time, its highest percentage of crystallinity was confirmed (T (179.8 °C) - the lowest thermal stability. Keshk et al. [T-values, which correlated with the degree of crystallinity of corn starch-cellulose mixtures, depending on the mixture ratio. They found an inversely proportional relationship between ΔT values and ΔCp values. Our ΔCp studies confirmed such a relationship outside of the SM2 samples having the lowest ΔCp value (0.465 J g-1·K-1) according to the lowest ΔT (Table S1). This phenomenon may be supported by the observation of Liu et al. [Tg leads to the reorganization of the structure. In our study, SM1, SM2 and SM3 starches were hardened at 2 degrees below Tg. Moreover, the research is confirmed by PXRD studies, where SM2 had the highest degree of amorphousness  at 157 °C and slowest for SM3_MB (adsorbed) at 163 °C  and 5f.et al. [et al. [k2 values obtained for SN, SM3, SM2 and SM1 were 1.9623, 1.5367, 0.9240, 0.1848 g mg-1·min-1, respectively, and occurred at a weight of 1000 mg for SN and SM1 and 2.0 mg for SM2 and SM3 (Table S2). This suggests that MB adsorbed fastest on SN, but this was dependent on the surface area of the adsorbent: the larger the surface area, the faster the saturation. In the case of SM2 and SM3, MB adsorbed fastest already on the smallest starch particle, suggesting a high affinity of the adsorbate to the adsorbent. The slowest adsorption was observed in SM1.MB adsorption on starch predominantly matched a pseudo-second-order model. A study by Lin et al.  of MB ad [et al.  matched q values in Table S2 differ from qm obtained according to Langmuir and BET isotherms. Moreover, some of the Langmuir adsorption constants (qm and KL) showed negative values  in mesopores, (2) via bonds between -OH and C=O and Pb2+, (3) H+ from the acidic carboxyl and phosphate groups is replaced by the Pb2+ ion.The MB adsorbed on the 1000 mg of the starch decreased according to the following pattern: SN > SM2 > SM3 > SM1 . The hydet al.  observed [et al.  after stet al. [et al. [An additional element worth highlighting is the effect of the method of preparation of modified starches on their structure. Two of our SM2 and SM3 starches were prepared using the crosslinking method, which provides increased starch stabilization by forming bridges between hydroxyl groups and anhydroglucoses . Kapelkoet al.  found cr [et al.  used cor [et al. . In the  [et al. .via electrostatic attraction between the heterogeneous adsorbent surface and the adsorbate molecules, as indicated by the Freundlich adsorption model.Citrate starch (SM2) presented the best adsorption capacity, followed by phosphate starch (SM3), and acetylated starch (SM1) showed the weakest adsorption capacity, according to the adsorption results. MB adsorption on SM2 and SM3 followed the BET isotherm model, which shows the physical adsorption of the cationic dye on the low-porous surface. In the case of SM1, MB adsorption probably took place - groups of SM2 starch and N+ of MB was revealed on the basis of characteristic vibration of the C=N bond in the adsorbent derived from MB at 1600 cm-1.Spectral methods confirmed the adsorption capacity of SM2. In the FTIR study, a probable interaction between the OHvia the existence of an exothermic peak belonging to MB in samples of these two adsorbents. The appearance of endothermic peaks in the case of SM2 and SM3 samples with adsorbed MB in the second heating cycle confirmed the interaction between the adsorbents and the adsorbates. The effect of the manufacturing method of the modified starches on their durability was noted. SM2 and SM3 starches were prepared using the crosslinking method, which provided increased starch stabilization.The DSC results showed the lowest thermal stability of SM2 correlated with its amorphous structure. Moreover, according to DSC, the possibility of interaction between SM2 or SM3 and MB was demonstrated The above presented properties and, in addition, the “safe chemistry” and slightly acidic pH of the tested starches make them useful, especially citrate starch, as carriers for biologically active substances containing cationic groups, in topical skin applications. The present research confirms our previous studies on lidocaine hydrochloride. The modified starches may be the potential carriers in the form of hydrogels, similar to the hydrocolloid dressings.


Autologous split-thickness skin grafts (STSG) have been the gold standard concerning treatment of both full and partial thickness burns. However, new treatments are emerging such as spray-on autologous keratinocyte suspensions (AKS). AKS is composed of a patient’s own keratinocytes, fibroblasts, and melanocytes. STSG in conjunction with AKS has showed promising results for the treatment of deep partial and full thickness burns. The standard graft takedown with STSG has historically been on postoperative day (POD) 6. Once graft take down occurs, occupational therapy can begin along with cessation of continuous splinting leading to more convenience and comfortability for the patient. With AKS, we propose that faster wound healing allows graft take down to occur earlier on POD 4.A retrospective chart review was conducted at TTUHSC’s UMC Burn Center looking at patients that underwent STSG with AKS for the treatment of their burn. Patients were divided based on graft takedown on POD 4 or 6. Factors such as time to complete wound closure and postoperative complications were analyzed.The average age and total burn surface area in both groups was 43 years old and 28%. In the POD 4 group (n=10) the average time to healing was 2 months while the POD 6 group (n=21) had an average healing time of 3.1 months. Graft take and percentage of patients with changes in postoperative mobility and hypertrophic scarring were the same across both groups .While time to healing was slightly different, there was not a statistically significant difference between the two groups. From this study we can conclude that graft take down can occur earlier in patients treated with STSG in conjunction with AKS. This allows for earlier therapeutic intervention and more convenience for the patient. • Graft takedown can occur earlier on postoperative day 4 instead of 6 in patients treated with split-thickness skin grafting in conjunction with autologous keratinocyte suspension. • Earlier graft takedown allows for earlier occupational therapy and reduces the need for extensive splinting which can be uncomfortable for the patient. • Graft takedown can occur earlier in those treated with split-thickness skin grafting in conjunction with autologous keratinocyte suspension due to the rapid healing the suspension offers.


The frontal eye field (FEF) and the inferior frontal junction (IFJ) are prefrontal structures involved in mediating multiple aspects of goal-driven behavior. Despite being recognized as prominent nodes of the networks underlying spatial attention and oculomotor control, and working memory and cognitive control, respectively, the limited quantitative evidence on their precise localization has considerably impeded the detailed understanding of their structure and connectivity. In this study, we performed an activation likelihood estimation (ALE) fMRI meta-analysis by selecting studies that employed standard paradigms to accurately infer the localization of these regions in stereotaxic space. For the FEF, we found the highest spatial convergence of activations for prosaccade and antisaccade paradigms at the junction of the precentral sulcus and superior frontal sulcus. For the IFJ, we found consistent activations across oddball/attention, working memory, task-switching and Stroop paradigms at the junction of the inferior precentral sulcus and inferior frontal sulcus. We related these clusters to previous meta-analyses, sulcal/gyral neuroanatomy, and a comprehensive brain parcellation, highlighting important differences compared to their results and taxonomy. Finally, we leveraged the ALE peak coordinates as seeds to perform a meta-analytic connectivity modeling (MACM) analysis, which revealed systematic coactivation patterns spanning the frontal, parietal, and temporal cortices. We decoded the behavioral domains associated with these coactivations, suggesting that these may allow FEF and IFJ to support their specialized roles in flexible behavior. Our study provides the meta-analytic groundwork for investigating the relationship between functional specialization and connectivity of two crucial control structures of the prefrontal cortex.The online version contains supplementary material available at 10.1007/s00429-023-02641-y. Owing to the capabilities that likely derive from the massive expansion in the cortical surface area allowed by the folding patterns of the cortex , two lateral structures, the frontal eye field (FEF) and the inferior frontal junction (IFJ) have largely overlapping but complementary roles, being involved in several orchestrating functions such as attention, working memory, cognitive control, and other top-down processes , rather than as full activation maps accompanied by a statistical summary of the effect sizes, and even more rarely shared in that form . In the ALE procedure, each set of foci reported in a study is modeled as a three-dimensional Gaussian distribution centered around the coordinates and whose width is determined based on the experiment sample size . We searched records in the Web of Science Core Collection using the keywords ‘fMRI’ AND ‘frontal eye field’  in the first instance, and ‘fMRI’ AND ‘inferior frontal junction’  in the second. We complemented these results with other sources  by one of the authors (MB). In the FEF sample, our search identified a total of 711 records, from which we removed all the review papers. 665 records were further screened, and 470 of these were sought for retrieval to assess their adequacy with respect to the inclusion criteria (described below). In the IFJ sample, 375 results were identified, from which, after removing the review papers, 356 records were further screened, and 142 of these were sought for retrieval to assess their adequacy with respect to our inclusion criteria. The general inclusion criteria consisted of the following. Each study selected: 1. Reported coordinates in standard space ; 2. Was an fMRI study  as well as covert spatial attention using both blocked and event-related designs  and endogenous cueing paradigms), working memory . The number of experiments was 35 for the FEF localizer sample, and 32 for the IFJ localizer sample. Both sample sizes were within the recommended range  to have adequate statistical power with ALE as derived from empirical simulations . Compared to cluster-level FWE inference, which can only allow inferring that a given cluster is above a significance threshold as a whole, but critically, not that any putative region that is included in the cluster is individually significant on its own, voxel-level FWE allows to more readily interpret all the cluster extent as well as its peak location from the main localizer samples anatomically , the ALE parameters were set to 5000 threshold permutations and a voxel-level FWE of 0.01 was applied , but not reported simply due to the lack of whole-brain brain coverage. The inclusion of studies with partial brain coverage is, however, generally not recommended in ALE analyses . Once we mapped the ALE clusters to this surface, we also mapped the MNI152 coordinates corresponding to each ALE peak to a vertex on the inflated surface and we assigned each of these to the respective MMP1 labels .The studies were retrieved from the BrainMap database using Sleuth according to the following fields : in the Experiment field, the “context” field was set to “normal mapping”, in the “activation” field we searched for “activations only”, with “Imaging modality” being set to “fMRI”. Finally, four separate searches were conducted in the BrainMap database by setting the “locations” field as corresponding to each left hemisphere (LH) and right hemisphere (RH) seed region . We first transformed each seed location from MNI152 to Talairach space  using the transformation by Lancaster et al.  but alsoIn the FEF localizer sample, the activations converged most strongly in two main clusters localized in the left and right posterior dorsolateral PFC. Two ALE peaks were found near the junction of the sPCS with the SFS, localized in the anterior (in the LH) and posterior (in the RH) banks of the sPCS Fig. . These pIn the left hemisphere, we also found a lateral peak within the main FEF cluster, which was localized on the bank of the iPCS, dorsal to its junction with the IFS see Fig. . This laIn addition to the main IFJ clusters in the left/right PFC, we found seven consistently activated clusters forming a broad frontoparietal network see Fig. . In the The results of the control analysis excluding studies with partial brain coverage replicated our main localizer results, both in terms of the inferred ALE peaks and of the number and anatomical localization of the significant clusters. In the FEF sample, the ALE peaks coincided with the main analysis, and the only difference that emerged was a minor shift of the left iFEF peak . In the IFJ sample, the ALE peaks again coincided with the results of our main analysis. The results of the second control analysis where we excluded all ROI analyses again matched well with our main results in terms of the inferred ALE peaks but had important differences regarding the number and anatomical localization of the significant clusters. The results of this control analysis are reported in the Supplementary Information . In the FEF sample, the cluster with the highest ALE value was now localized primarily in the left medial frontal gyrus, including the SCEF. The second and third larger clusters were, however, again localized in the left and right FEF, respectively, and were of the same size approximately. The inferred FEF ALE peaks were almost identical to our main results . However, in contrast to our main analysis results, the left FEF cluster did not spread onto the iPCS and there was not any peak corresponding to the iFEF. Interestingly, we also found a second peak in the right FEF cluster, which was lateral relative to the first, although still localized within the sPCS . In the IFJ sample, the most prominent clusters were localized in the left and right IFJ, replicating our main analysis results. Their ALE peak exactly matched those from our main results in the left hemisphere  but had some slight differences in the right hemispheres, where we found two peaks with similar ALE values . This indicates some residual variability in the localization of the right IFJ, which was not detectable in our main analysis results.post-mortem studies , to areas that are involved in maintaining and updating behavioral goals according to internal representations Below is the link to the electronic supplementary material.


During the different steps of bread-making, changes in the microstructure of the dough, particularly in the gas cell walls (GCW), have a major influence on the final bread crumb texture. Investigation of the spatial conformation of GCWs is still a challenge because it requires both high resolutions and 3D depth imaging. The originality of the present work lies in the use of label-free non-destructive multiphoton microscopy (NLOM) to image the 3D structure of GCWs, shedding light on their behavior and organization in wheat bread dough. We demonstrated that second and third harmonic generation  allow imaging, respectively, of starch granules and interfaces in bread dough, while the gluten matrix was detected via two-photon excitation fluorescence (TPEF). Last, a distinction between the gluten network and starch granules was achieved using gluten endogenous fluorescence (EF) imaging, while the position, size, and 3D orientation of starch granules in GCWs were determined from harmonic imaging, made possible by the acquisition of backward and forward SHG with linear polarization. These innovative experiments highlight the strengths of NLOM for a label-free characterization of bread dough microstructure for the first time, in order to understand the role of starch granules in dough stabilization. To this end, a variety of microscopy and imaging techniques have been applied over past decades to provide a picture of bread dough microstructure.In order to better understand and optimize the macroscopic behavior of bread to improve its quality, it is essential to evaluate the microstructure of the dough—in other words, the microscopic organization of its major constitutive components: gluten, starch, and gas cells—as well as the changes it undergoes during the different processing steps of breadmaking2 evaluated bread dough microstructure from mixing to baking using LM. Just over a decade later, Sandstedt et al.3 combined LM with staining of the gluten network, revealing the organization of the gas cell walls (GCWs) in bread dough just before and after baking. Thus far, this is the only set of images available in the literature for thin GCWs . Huang and Moss4 also combined LM and protein staining to describe the mechanisms involved in the development of the gluten network in steamed bread dough. Hug-Iten et al.5, instead of staining proteins, combined LM and staining of the starch granules (using iodine vapor) to qualitatively evaluate their morphology in dough and in fresh and aged breadcrumbs. There are many other examples of the use of such techniques. However, despite all care being taken in sample preparation, the fixation, dehydration, and/or staining steps can interfere with a sample’s microstructure, leading to misinterpretation of the images7.Conventional light microscopy (LM), for example, was used to describe the organization and development of different components in bread dough before and after baking. In a pioneering study, Burhans and Clapp8. Amend and Belitz10, for instance, published several works using electron microscopy in breadmaking, thoroughly evaluating the spatial organization of gluten. In turn, Roman-Gutierrez et al.11 used SEM and environmental SEM (ESEM) to reveal the initial microstructure of different wheat flours and their main components , as well as their changes during hydration. In a more recent study, Yang et al.12 combined SEM with several other analytical methods to suggest that starch played a more decisive role in mixing properties than gluten. In all cases, preparation of the samples  and the lack of staining to differentiate the components, especially for complex compositions as in bread dough, present major obstacles to establishing firm conclusions from electron microscopic observations11.Electron microscopy techniques, such as scanning electron microscopy (SEM) and transmission electron microscopy (TEM), have also been widely applied in bread science because of better imaging resolution compared with LM13. Turbin-Orger et al.14, or Dubreil et al.15, for example, used CLSM combined with multilabeling to identify lipids and proteins in the liquid films that separate the gas cells in wheat flour dough, or gas cells, lipids, and puroindoline-a simultaneously in bulk dough. Quantitative analysis has also been conducted on CLSM images with specific staining. Bousquieres et al.16, for example, quantitatively evaluated laminated dough to differentiate fat from dough layers while Jekle and Becker1 evaluated the effects of added water on the microstructure of dough proteins, characterized by average size, area fraction and perimeter, among others. It is important to mention that, despite some pioneering studies, CLSM images of dough in the literature are mainly the object of qualitative evaluation. Once again, sample preparation can be a drawback17.One alternative is to use Confocal Laser Scanning Microscopy (CLSM) with appropriate staining  to visualize bread dough microstructure with good imaging resolution. This method offers the possibility of depth imaging and 3D reconstruction of the sample—although this last feature has rarely been exploited19. Conceptual schemes for phase organization in GCWs at these advanced processing stages have been proposed by some authors21, relying on the small number of microscopic images of thin GCWs available until now14. In addition, the exact role played by starch granules in GCW (de)stabilization is not yet clear, while we do know that GCW mechanical properties partially depend on the proportion and location of starch granules in the GCW19. More information could be acquired by microscopy, ideally with 3D exploration given the geometry of these granules. It also seems clear from the review above that, to gain a more accurate visualization of the spatial organization of the dough’s main components  in GCWs, a microscopic technique that combines good image resolution with minimal sample preparation and a label-free approach is desirable. Raman microscopy and non-linear optical microscopy (NLOM) are good candidates in this regard.Despite the various imaging techniques available, the literature review revealed that the majority of studies on dough microscopy have focused on bulk dough at the mixing or shaping steps (dough development). Very few studies focus on the latter stages of breadmaking, where the dough has been stretched between bubbles of increasing size, despite the fact that the degree of extension and the moment of GCW rupture are known to be crucial for good aeration and fine texture in crumb22 have already applied Raman spectroscopy to mixed dough at the frozen state, with the objective of disentangling the signals from ice, liquid water, starch, gluten and yeast with the aid of reference spectra for these pure components. NLOM, for its part, has never been applied in dough evaluation before, despite having been successfully used to image a large range of label-free biological samples23 using Second Harmonic Generation (SHG) and Third Harmonic Generation (THG) contrast exploration. Use of SHG from collagen or myosin bands in muscle26 and from amylopectin in starch granules28 has been described in the literature. Furthermore, SHG was used to image starch granules in plants30 and starchy food products31, with promising results. THG from local transitions in the refractive index produces contrasts that can be used to explore heterogeneity in materials and the interfaces between phases in particular33. Thus, harmonic microscopy has the potential to be a valuable technique in evaluating label-free bread dough, with SHG imaging of the starch granules and THG imaging of the starch-dough-gas interfaces. Last, with regard to 3D analysis, NLOM is also advantageous in terms of penetration depth, since the infrared excitation wavelengths reduce light scattering and sample absorption35.Huen et al.In the present work, an innovative approach based on non-destructive NLOM was applied to label-free proven dough. The objective of this pioneering study was to analyze the spatial dispersion of phases in proven dough, especially starch granules and gluten network in GCWs, and also examine the morphology of the GCW envelope and these embedded starch granules, providing new microscopic information to support the hypotheses about the role played by starch granules in GCW (de)stabilization. To that end, we developed and assessed a non-destructive multiphoton microscopic approach, compatible with the bio-distribution analysis of the starch granules in GCWs at different stages of extension, using both two-photon excitation fluorescence (TPEF mode) and harmonic generation signals  obtained from the label-free dough. The potential for a depth-based (3D) exploration of dough microstructure at different scales (that of gas cells and that of GCWs) under near-infrared multiphoton excitation was further evaluated. This was the first use of NLOM to image dough and GCWs, and has demonstrated its potential to expand knowledge in the breadmaking field.Firstly, a multimodal study of label-free dough was performed by combining THG, SHG and endogenous fluorescence (EF) acquired using NLOM, to better explore the heterogeneity of this material and the interfaces between its components. Despite promising results, the evaluation of the 3D organization of the GCWs was impaired by the THG mode (as better discussed below). After some further adaptations, SHG and EF were combined to image a number of thin GCWs, evaluate their 3D organization, with respect to starch granules in particular, and provide information on some of the morphological features of these starch granules.A number of different typical image stacks acquired from proven dough are described in this section to illustrate the exploration pathway and of its major outcomes. Table The dough phases, i.e. the aqueous granular phase (mostly composed of starch granules), the hydrated gluten network and the gaseous phase dispersed across numerous cells of increasing size, were imaged using a multimodal and dual excitation approach (1240 nm/1040 nm) to acquire EF, THG and SHG combined signals, with backward mode detection. THG was acquired in the blue channel , EF was acquired in the yellow channel  and SHG was acquired in the remaining two available channels: green  and red channels   were revealed  displayed a fibrillary texture as could be expected for the gluten network  image acquisition performed at 0°, 45°, 90° and 135° showed polarization anisotropy of the starch granules, with mutually complementary SHG signal information detected in different areas of the granules. In each starch granule, two lobe structures appeared to be oriented parallel to the linearly-excited polarization direction , B-type granules (5–15 μm), and C-type granules (< 5 μm).The summed reconstruction of linear polarization images obtained from the four linear polarization angles Fig. c made po39. In addition, the SHG imaging of starch granules depicted black centers to the granules, as shown by the white arrows in Fig. 40.B-SHG appeared more intense at the periphery of each starch granule than in the inner part in comparison to F-SHG  and SHG in the blue channel 415/10 nm)  and a few granules, located at the edges , while GCW3 and GCW4 were considered to be intact walls . To illustrate this, a depth-based view of these strings and intact walls is provided in Fig. Before commenting on the different modes displayed in Fig. 43. Our observations suggested that some tiny GCs might remain even after extensive stretching of the GCW.As stated above, it was possible to discern both granular and gluten phases (the latter being assigned to areas of high autofluorescence intensity) in the walls/strings studied. For the first time and with no labelling, we were able to examine a thin wall (GCW4), consisting of one row of starch granules and to detect the initiation of gluten films at two locations between these granules; it was also possible to examine longer threads of gluten containing no starch granules, deemed to be the remains of a GCW rupture in the GCW1 string. Starch granules were at their smallest within the wall; this was markedly the case for GCW3, but with some exceptions in GCW4 , see “38 (white stars). This elongation of the starch granules along the z-axis probably results from an optical aberration caused by the inhomogeneous distribution of harmonophores within spherical starch granules44 and by the index-mismatched interfaces found in bread dough45.The segmentation of the starch granules was facilitated by (i) the specificity of SHG imaging to starch granules, with no background images, (ii) the perfect outline of the starch granule obtained through SHG polarization imaging. However, because the SHG signal decreased more strongly with depth than the EF signal, starch granule segmentation was not performed across the full depth of the stack Fig. a3–d3. OnA threshold was set in the EF mode Fig. a2–d2 to The sum of segmented starch pixels divided by the sum of segmented envelope pixels yielded an estimate of the starch fraction in each GCW: 61.5 ± 2.0% (GCW1); 49.3 ± 7.6% (GCW2); 56.9 ± 13.6% (GCW3); and 54.3 ± 3.7% (GCW4). The standard deviations relate to the extreme variability in the size and number of starch granules to be found in the slices evaluated at different depths within the image stacks. The high averages and dispersion of the values will be discussed further in the next section.38, also classically acquired in 2D.The size distribution of starch granules in the GCWs was also calculated using the starch segmentation data and the results are presented in Fig. 38, 6–7 and 24–25 µm respectively. The main difference lay in the peak position of the smallest granules—whereas Wilson et al.38 recorded a narrow peak at 1–2 µm, we encountered a wide peak at 4 µm. This could be because the detection of these granules using NLOM is defective . The same reason could explain the higher volume proportion for A-type granules . Differences in wheat varieties and milling procedure between the studies might also explain the difference in C-type granule distributions and further investigation to differentiate between factors would be required before any clearer conclusions can be drawn about the possible limitations of NLOM.As expected for wheat starch, size distribution was trimodal in the GCWs observed in the present study, with peaks at around 4 µm, 6 µm and 22 µm for C-, B- and A-types, respectively. Note that in Fig. In this work, multimodal acquisition combining EF and harmonic generation signals from a label-free proven bread dough was used for the first time to analyze the spatial dispersion of starch granules and gluten network in GCWs, also examining the morphology of the GCW envelope and these embedded starch granules. This innovative approach was selected as it allows the live imaging of proven dough without the need for labeling, thanks to endogenous fluorescence and harmonic generation assignable to protein and starch respectively. High autofluorescence intensity allowed imaging of the gluten phase, second harmonic generation imaged the starch granules and third harmonic generation imaged the interfaces of bread dough, while the gas cells could be detected using contrast . Third harmonic generation was not further analyzed, however, because of its lack of contrast in the depth of acquisition.In contrast, second harmonic generation and endogenous fluorescent measurements demonstrated their strong potential as methods for the 3D imaging of bread dough. The dual excitation 1040 nm/1240 nm allowed to acquire SHG of whole starch granules without doing any supplementary acquisition with linear polarization. Nevertheless, a more standardized protocol was selected to be used in our main experiments, applying a single excitation wavelength (820 nm) combined with controlled linear polarization. In this way, a larger scientific community could benefit from the protocol here applied.20, which serves as a reference for the cereal chemistry community, where it is suggested that the gluten is concentrated outside and starch inside the GCWs.Through these experiments, we observed that the gluten phase appeared to twist between groups of starch granules, suggesting that in GCWs, the two phases are still interwoven with each other. This spatial organization differs from that proposed by Eliasson and Larsson19. The presence of gluten independently of starch was observed only in our study of strings—that is, once rupture had taken place. Indeed, once a gluten film has reached the size of starch granules it becomes instable, meaning that there is very little chance of it being observed under the microscope. This also explained a degree of blurring over the full length of GCWs whose thickness approaches that of lenticular granules (20–30 µm), such as GCW4 . This contrasts with what can be assumed to be an important role in GCW stabilizationCW4 Fig. c4. Last,CW4 Fig. . Indeed,28. The combination of forward and backward detection with four polarization angles allowed individual granule segmentation, paving the way for highly specific quantitative analysis of the granular phase, such as the determination of the starch fraction and granule size distribution in GCWs. It is important to emphasize once again that the combination offered by four-angle polarization provided more reliable access to the morphology and size of the starch granules, without which smaller granules (C- and even B-type) could have been missed completely. In addition, harmonic imaging has the particular quality of not being sensitive to photobleaching24 enabling long acquisition times without loss of signal.Starch granules were detected in SHG images independently of the other dough components thanks to the high specificity of the harmonic signal limited to the non-centrosymmetric structure of amylopectin3 when demonstrating the imaging of thin GCWs for the first time using optical microscopy combined with the staining of proteins. Meanwhile, it is consistent with SEM observations of the internal surfaces of gas cells where starch granules presented their largest sides perpendicular to the direction of dough extension46.Once the granules had been segmented, it was possible to observe that not all of the largest starch granules preferentially orientated their longest sides along the GCW direction of extension. This observation contrasts with the neat alignment reported by Sandstedt et al.47 estimated that starch granules occupy about 60% of the dough’s volume, being this approximation based on a calculation using flour and dough composition as the reference, but very few details on the exact calculation steps were given. A similar, but more detailed approach was used by Tanner et al.48, who obtained values for the starch volumetric fraction in bulk dough that varied from 46 to 60%. The different values were due to variations in dough water content and in the consequent swelling of the starch through water absorption. Tanner et al.48 also took other factors into consideration to obtain these values, including the starch fraction in the flour, the percentage of damaged starch granules (a major influence), dough and starch densities, and the fraction of A- and B-type starch granules. The use of calculation, despite the many hypotheses it requires, can be useful to estimate the starch fraction when no other means are available. A pioneering quantitative analysis of microscopic images of dough was carried out by Mohammed et al.49 in 2013. They used cryo-SEM images of non-yeasted bulk dough for this purpose. However, comparison between original and treated images (both provided in the paper) clearly showed that the starch content was underestimated , possibly explaining the low value reported for the starch fraction (45%) in their paper. Following on from this initial work by Mohamed et al.49, we analysed NLOM images of GCWs after manual segmentation of the starch granules to estimate the starch fraction in the GCWs. Overall, a starch fraction of 56 ± 9% was found .Starch fraction has been rarely estimated in the literature, even for bulk dough. Bloksma47 and Tanner’s48 calculations. On the other hand, in thicker GCWs, the starch granule fraction ranged from 33 to 65%. For the “strings” it is possible that, following rupture, the starch granules agglomerate at the edges. Here, the presence of some granule-free areas would be compensated by the greater fraction of granules at the edges, giving a somewhat higher average value for strings. For “thick” GCWs, the high level of variation can be explained by the variability of the presence of starch granules at different depths. Despite such variations, this order of magnitude has been estimated experimentally for GCWs for the first time . This finding will require further exploration under different conditions (different dough preparations for instance) with a larger number of samples, but already, this order of magnitude is of utmost importance for the initialization and validation of numerical models reproducing the mechanics of GCWs, such as that described in Mohammed et al.49 or Dedey et al.50.The high variation in these values can be explained by the particularities of each GCW analyzed. Thus, on the one hand, string shaped GCWs containing areas of gluten that were free of starch granules yielded starch fraction values from 59 to 64%; this range being consistent with Bloksma’s38, suggesting that the extension of the dough would not involve the full segregation of certain starch types.Our study also demonstrated that the size distributions of granules, as analyzed in GCWs from NLOM measurements, were similar to those reported for wheat flourBecause of the deformation of the granules along the z-axis (depth) that resulted from optical aberration, the entire quantitative analysis for starch was performed in 2D in the first instance. Alternative solutions could be investigated to decrease the elongation of starch granules along z-axis, such as work on refractive index matching. Image analysis in 3D will also require the setup of an automatic segmentation routine. The 3D value of our microscopic investigation of GCWs was uncompromised in the EF mode. In fact, the exploration depth of the EF  exceeded in 2 to 4 times the thickness of the largest granules of starch. It was found to be useful in distinguishing strings from intact walls  the specific separation of starch granules from the gluten network in bread dough by using two investigative acquisition modes—TPEF for endogenous fluorescence imaging and SHG for starch granule imaging, and (ii) the non-invasive analysis of the microstructural features of bread dough and gas cell walls, thus demonstrating that this innovative method can be successfully applied in dynamic imaging. In particular, this new approach has real potential for the monitoring of GCW microstructure during proving. With the help of future work to improve the fast imaging parameters and the use of AI to facilitate the 3D segmentation of starch granules for image analysis, this is a real prospect.The different steps of sample preparation and NLOM analysis are illustrated in Fig. The bread dough was prepared according to the following recipe: 245.0 g of wheat flour , 147.0 g of water, 4.9 g of salt and 2.49 g of powdered yeast (Lesaffre Saf-Instant yeast). The ingredients were mixed using an alveograph , using the following protocol: the powders were blended for 1 min, after which the water was added and the ingredients were mixed for 1 min 30 s. The equipment was then turned off and ingredients adhering to the walls of the chamber were removed and manually mixed with a spatula. The dough was then kneaded for 10 min. After mixing, the temperature of the dough was 25 °C ± 1 °C.Dough pieces (15 g) were sampled, placed inside graduated plastic pots, and stored overnight under refrigeration (dough core temperature was 1.0 °C ± 0.3 °C). Volume increase was less than 1.37 (+ 37%) overnight, indicating that proving was very limited because of the low temperature. The dough was transported in a cooler to the microscopy platform and kept cold once there. Very small dough samples (occupying about a quarter of the glass coverslip section once in place on the spacer) were removed regularly from the pots throughout the day of the experiment and were flattened between a glass slide and coverslip separated from each other by a 250 µm spacer Fig. . A smallUsing optical microscopy, two different types of gas cells could be observed in the dough sample at advanced stages of proving  coupled with an Insight Deepsee laser , used in the 820–1300 nm range < 120 fs pulse width . An auxiliary beam at 1040 nm was available in combination with the tunable output for dual wavelength excitation. For all images, an apochromat 25X MP1300 immersion objective was employed  and distilled water was used as the immersion medium. A half-wave plate  was rotated to control the laser polarization angle in the range 0°–135°. The microscope was equipped with eight Non-Descanned Detectors (NDD), three GaAsP  and one photomultiplier tube for backward detection, with the same detectors also configured for forward detection.The endogenous fluorescence (EF) was probed with Two Photon Excitation (TPE). SHG was detected in both backward and forward channels, whereas THG was detected only in the backward channel. Different dichroic and emission filters from Semrock were used according to the excitation wavelengths and analyzed signals . More information on the acquisition parameters can be found in Table The images that appear in the figures in this paper were treated using NiS-Elements software .Segmentation was carried out using Avizo software (Thermo Fisher Scientific). Segmentation of the starch granules was performed from images combining all modes (EF and forward and backward SHG with four polarization angles). The method consisted of manually defining the edges of a given starch granule  in the x–y plane of the image by using a brush tool, where each pixel was assigned to an object (label). Each successive slice was then swept in the z-direction (depth of the image stack) so that the reconstruction of the granule could be as faithful as possible. To speed up the segmentation process, some slices were omitted, and these missing intermediate slices were automatically interpolated by Avizo (interpolate function). The sequence of operations was then repeated for another granule. Visual scrutiny of the slices while carrying out the granule segmentation made it possible to determine the effective SHG-mode depth analyzable by manual segmentation, being 15.5 µm of analyzable depth for the GCW1, 15 µm for GCW2, 7.5 µm for GCW3 and 15 µm for GCW4. The selected slices were the same considered as candidates for the 3D representation in Fig. 38 retains the longest dimension for each granule identified from 2D images and, assuming the shape to be spherical, converts this dimension into granule volume in order to visualize B- and A- starch types (see “For each object (granule), it was then possible to obtain individual quantitative data, such as position, volume and dimensions, using the “Label Analysis” tool from Avizo. The classic method to estimate granule size distributionpes see “” sectionFollowing segmentation of the starch granules, described above, it was also possible to calculate the surface area occupied by the starch in the wall, known as the starch fraction. As in the previous analysis (granule size distribution), this calculation was performed in 2D, but repeated on five slices for each GCW instead of only one. To be comparable, these slices were of the same number (five) and positioned across the same total depth for all GCWs (6 µm). For this purpose, the number of pixels defined as “starch” were added together and this value was divided by the total number of pixels for the GCW envelope, determined by applying manual thresholding to the autofluorescence images. A variation of 40% in this thresholding, representing a total variation in GCW thickness of 3–4 µm, yielded a variation of 5% in the starch fraction. This value was lower than the variation due to the slice position (and the number of starch granules between slices). The calculation was performed on five slices for each GCW so that the variation of granule numbers in the depth direction could be taken into consideration, yielding a mean value and its standard deviation. Additionally, the first slices of each domain were discarded to avoid a border effect in the granules (where size/number of starch granules are not representative). The selected stacks for the starch fraction calculation started from different slices for each GCW.Supplementary Figure S1.


Simultaneous pancreas kidney (SPK) transplantation is an invaluable procedure to enhance the quality of life of insulin-dependent patients with advanced renal disease. The creation of vascular anastomoses of the donor’s pancreas vessels to the recipient’s, is of utmost importance to predict the graft outcome and surgical complications. In the study we introduce a novel technique for arterial reconstruction during SPK transplantation.Conventionally, during the SPK transplantation, a so-called Y-graft is anastomosed between donor’s superior mesenteric and splenic artery to the recipient’s right iliac artery. In the study we adopted a new technique by preparing an extra extension using the donor’s carotid artery, to be anastomosed to the Y-graft and the iliac artery. In this non-blinded randomized clinical trial we compared the surgical complications and early outcomes between the 2 groups of patients with the traditional and new arterial reconstruction techniques during 3 months after transplantation.Thirty adult patients were included in the study. The incidence of pancreatitis, vascular thrombosis and surgical site infection was lower in the new Y-graft and extension technique, which was not statistically significant. However, the calculated Cohen’s d index showed the medium effect of new Y-graft and extension technique on complication after SPK transplantations.The post-operative complications tend to be lower in the novel arterial reconstruction technique, however a study on a larger patient group is encouraged to confirm our primary results.http://www.irct.ir/).The study was registered at the Iranian Registry of Clinical Trials on 12/05/2022; IRCT 20210625051701N2; ( In 1966, university of Minnesota performed the first pancreas transplantation as a physiologic treatment of diabetes mellitus (DM). Ever since, improvements of surgical techniques and introduction of new immunosuppressant therapies have helped the procedure gain more desirable outcomes . Less thDespite over two decades of experience and considerable refinements in the surgical techniques, the operation still carries a high morbidity. The majority of technical injuries to the pancreas result from pancreas devascularization during the combined liver-pancreas procurement . The artThe Abu Ali Sina solid organ transplantation hospital is a major center of its type in the Asia, performing more than 500 annual solid organ transplants, including pancreas . In thisThe study was conducted as a non-blinded randomized clinical trial in the Abu Ali Sina solid organ transplantation hospital which affiliated to Shiraz University of Medical Sciences, Shiraz, Iran from July of 2022 to December of 2022. The simple randomized method was used to randomize the study subjects. The participants included those of 18 years old or above, suffering from diabetic nephropathy who met the SPK criteria of our center . The donhttp://www.irct.ir/).The protocol and patient informed consent form were reviewed and approved by the local Ethics Committee of Shiraz University of Medical Sciences (IR.SUMS.MED.REC.1401.045). The study was registered at the Iranian Registry of Clinical Trials  to the root of the mesentery and after SMV venotomy, created an end-to-side portal vein to SMV anastomosis with the standard vessel anastomosis technique. For arterial reconstruction the patients were randomized to two groups: we used either the Y-graft technique  or the Y-graft and extension technique (the new technique to be investigated here). The arterial reconstruction in the Y-graft technique is yielded by the anastomosis of the Y-graft to the common iliac artery (CIA) Fig. -A.In the new technique of Y-graft and extension, the Y-graft is created with short limbs and one extension graft is prepared, using the carotid to be anastomosed to the CIA Fig. -B. ThereIn the study, we created portal venous drainage for outflow of the pancreas. Patients were randomly selected for either of the arterial anastomosis method. For the first group, the Y-graft was directly anastomosed to the right iliac artery and for the second group an extension artery was prepared using the carotid artery. The extension was then, crossed through the mesentery and anastomosed to the right iliac artery, close to SMV. For all patients, pancreas was transplanted prior to the kidney. According to our center guidelines, the kidney is transplanted via the same abdominal incision, in the retroperitoneum and on the left side.During the surgery we did not close the fascia and leave it to be done in the following one year. Patients underwent simultaneous appendectomy and they were transferred to the post-transplantation intensive care unit with 2 drainage tubes, one inserted near the kidney and one near the pancreas. All the patients were transferred to the organ transplantation intensive care unit after the operation. Methylprednisolone (1 g/day till 3 doses) and ATG (1.5 g/day till 4 days) were used as induction immunosuppression and then anti metabolites, CNIs and prednisolone were used as maintenance therapy. The desired Tacrolimus blood level is 8–12 ng/dL which was first checked on the 3rd day. Patients were then extubated as soon as possible.The procedure of SPK transplantation, similar to any major surgeries, carries a high risk of thromboembolism. The rotational thromboelastometry (ROTEM) test was requested for the patients, if the result showed a hypercoagulable state, anticoagulation with heparin was started during hospitalization and patients were discharged with Aspirin. But if it shows hypocoagulative or normal state, we do not give any anti-coagulants either during hospitalization or after discharge.Patients were visited by a multidisciplinary team including a nephrologist, the transplant surgeons and a clinical pharmacist on a daily basis for clinical evaluation and immunosuppressive adjustment. The daily renal function tests, blood sugar chart and pancreatic enzymes were meticulously evaluated. An abdominal ultrasound, as well as color Doppler evaluation of the graft vessels, were demanded every other day. Patients were weekly visited by a nephrologist and a transplant surgeon after being discharged from the hospital. Any complications or suspicious rejection were immediately referred to the hospital for further assessment and management. Any suspicious Rejection episode was diagnosed as the following algorithm Fig. .. The paThe current study is conducted to compare the incidence of the post-surgical complications including pancreatitis, vascular events, fistula formation, and rejection between the two methods in the first 3 months following the transplantation.Categorical variables were described as frequency rates and percentages, and continuous variables were described using mean, median, and interquartile range (IQR) or standard deviation (SD) values. Means for continuous variables were compared using the Mann-Whitney test. Proportions for categorical variables were compared using the χ2 test, and the Fisher exact test was used when the data were limited. To calculate the quantitative measure of effect size, the Cohen’s d index was measured for the qualitative outcomes of the two methods using mean and standard deviation. The Cohen’s d index of less than or equal to 0.2 was considered as a small size effect, whereas 0.5 and 0.8 results were considered as the medium and large size effects, respectively. All statistical analyses were performed using SPSS , version 16.0 software (SPSS Inc). A P-value of lower than 0.05 in the multivariate analysis was considered significant.A total number of 30 patients were included in the study. The patients were divided to 2 equal groups of 15, one group had underwent the Y-graft technique and for the other one the Y-graft and extension technique was applied. The mean age of patients was 33.1 ± 6.3 years. Among the patients, 19 were male and 11 were female. Table Table Pancreas transplantation is mostly done simultaneously with a kidney transplantation (SPK). In this study we have introduced a new arterial anastomosis technique, in which an extension is prepared using the carotid artery. The extension is then anastomosed between the conventional y-graft and the right iliac artery. Preferably, pancreas is transplanted prior to the kidney to reduce the duration of cold ischemic time and reduce the risk of consequent graft failure . Organ pIn the Y-graft technique, intestinal wall edema may occur due to SMV clamping and there is possibility of difficulty closing the abdominal wall. Furthermore; sometimes the Y-graft reaches the CIA difficultly, creating some tension. To avoid this tension, the splenic arm and superior mesenteric artery (SMA) are cut longer, which could contribute to kinking of the arteries and increased risk of thrombosis. In the common Y-graft technique, it is not possible to estimate the length of the Y-graft until the recipient’s anatomy has been explored, which sometimes inevitably lengthens the cold ischemic time. Therefore, in the Y-graft and extension technique we prepare the graft with an extra arterial extension to decrease the cold ischemic duration and as stated, the duration of operation tends to be shorter. For venous outflow, we used to prepare the graft for the portal drainage technique rather than the systemic one, this would result in a long Y-graft which might be kinked, increasing the risk of thrombosis . In contAnother difficulty of the Y-graft and extension technique is the need for an extra conduit. As a significant length of the donor’s iliac artery is preserved for the liver, the problem is solved by taking the required vascular conduit from donor’s carotid artery.Our results showed the incidence of pancreatitis and rejection was lower in the new Y-graft and extension procedure compared to traditional Y-graft technique. We assume that the mentioned results occurred due to less graft manipulation. As previous studies have reported a low tolerance of pancreas graft to the manipulation . ConsequPost-operative complications tend to be lower in the novel arterial reconstruction technique (Y- graft and extension), however a study on a larger patient group is encouraged to confirm our primary results.


In this workshop we aim to discuss the ins and outs of publishing in psychiatry with practical guidance from both sides of the couch. The executive editors of the European Journal of Psychiatry  will talk about their experience of starting a journal from scratch with very limited resources and share practical tips with the participants about publishing for early career psychiatrists to early career psychiatrists.None Declared


Oryza sativa L.) is the main source of energy for humans and a staple food of high cultural significance for much of the world’s population. Rice with highly resistant starch (RS) is beneficial for health and can reduce the risk of disease, especially type II diabetes. The identification of loci affecting starch properties will facilitate breeding of high-quality and health-supportive rice. A genome-wide association study (GWAS) of 230 rice cultivars was used to identify candidate loci affecting starch properties. The apparent amylose content (AAC) among rice cultivars ranged from 7.04 to 33.06%, and the AAC was positively correlated with RS (R2 = 0.94) and negatively correlated with rapidly available glucose (RAG) (R2 = −0.73). Three loci responsible for starch properties were detected on chromosomes 1, 6, and 11. On chromosome 6, the most significant SNP corresponded to LOC_Os06g04200 which encodes granule-bound starch synthase I (GBSSI) or starch synthase. Two novel loci associated with starch traits were LOC_Os01g65810 and LOC_Os11g01580, which encode an unknown protein and a sodium/calcium exchanger, respectively. The markers associated with GBSSI and LOC_Os11g01580 were tested in two independent sets of rice populations to confirm their effect on starch properties. The identification of genes associated with starch traits will further the understanding of the molecular mechanisms affecting starch in rice and may be useful in the selection of rice varieties with improved starch.Rice ( Oryza sativa L.) is one of the most important crops in the world and a main source of energy for humans, especially in Asia [Increasing economic and social development during the 20th century has increased aging in the world’s population. This trend will continue . Accordi in Asia . Starch, in Asia , whose f in Asia . AAC is  in Asia ,8. RAG r in Asia . The gly in Asia . Rapidly in Asia . SS can be divided into granule-bound starch synthase (GBSS) or Waxy (Wx), which regulates starch accumulation and is the main enzyme for the synthesis and elongation of amylose. Soluble starch synthase (SSS) is the main enzyme for amylopectin elongation [Starch is synthesized through the coordinated activities of multiple enzymes, including ADP-glucose pyrophosphorylase (AGPase), starch synthase (SS), and the starch branching enzyme (SBE) . AGPase ongation ,16. Addiongation . GBSSI controls the amount of amylose content [, starch synthase IIa (SSIIa), and isoamylase 1 (ISA1) are also associated with the RS content [A genome-wide association study (GWAS) is an effective method for predicting candidate genes based on the relationship between genetic and phenotypic variants. Presently, many genes are known to be responsible for rice starch properties.  content ,19,20 an content . Additio content ,23,24. MThe value of the starch property  was evaluated in 230 rice cultivars . The AACBased on clustering analysis of starch properties, the 230 rice cultivars were separated into five groups . Group 1Oryza sativa is shown in These cultivars were used for entire genome sequencing. The SNPs were distributed across the genome. The density of the SNPs in 12 chromosomes of p) = 6.84. Although other SNPs occurred on chromosomes 2 and 7, their significance level was lower. No significant SNPs were determined by the association of SAG, HI, and GI  SNPs are on chromosomes 1, 6, and 11 . Within this gene, the analysis detected a total of 14 SNPs located in 5′ UTR, exon 1, intron 9, and 3′end regions  is in block 5.To investigate the linked segments, we determined pairwise linkage disequilibrium (LD) between the most significant AAC-associated SNPs on chromosomes 1, 6, and 11 . SNP1004p value A. There ectively . Pairwis block 1 . SNP9020 block 1 A and it LOC_Os01g65810, LOC_Os06g04200, and LOC_Os11g01580, are shown in LOC_Os01g65810 (LOC_Os06g04200 resulted in the highest significant SNP (−log(p) = 41.77) at this position . The second molecular marker (marker II) was developed to genotype LOC_Os11g01580 . Two independent rice populations, each containing 100 cultivars, were used to study the correlation between the markers and the starch traits. The rice cultivar lists are shown in LOC_Os11g01580 . After amplification, two patterns, including a single band of 530 bp and triple bands of 570, 530, and 480 bp, were detected  was targeted to SNPs of exon 1 in detected . The tridetected E–H. 2. They were also correlated to the RS trait in both populations. Interestingly, for the RAG trait, both markers showed a much lower R2 in rice population I than in population II. It explained only 4–6% of the variation in population I, while it accounted for more than 30% of the RAG in population II. Neither marker could explain the SAG trait (Both markers can explain % AAC ranging from 0.50 to 0.85 RAG trait . Thai rice varieties displayed a wide variation in the apparent amylose content (AAC). The AAC of 230 rice cultivars ranged from 7.04 to 33.06% . All culWhen compared, the clustered group with the original location of the cultivars, group 1 and group 2, were mostly originally located in the north and northeastern part of Thailand. Most of group 4 and group 5, which contain a higher level of AAC, were originally from central Thailand . These aAmong these five groups, the rice in group 5 has the most desirable nutritional characteristics as indicated by the high RS and the low RAG/GI. The AAC was positively correlated with the RS content, suggesting that AAC may be the main factor determining the RS content in rice. Rice starch with a high AAC resulted in a high RS content in various rice varieties, consistent with the previous study ,21,23,32LOC_Os01g65810, which encodes an unknown protein. When compared to the reference japonica rice genome (Oryza sativa cv. Nipponbare), the selected indica rice cultivars in this study have SNPs and INDEL predicted to result in a truncated protein  and encodes plant glycogenin-like starch initiation proteins (PGSIPs). PGSIPs are predicted to be chloroplast localized, and a knockout of PGSIP1 in Arabidopsis decreased starch accumulation in the leaves [LOC_Os01g65790 encoded pectin esterase. Pectin is one of the main components of the plant cell wall, controls cell wall porosity, and constitutes the major adhesive material between cells [LOC_Os01g65810, as it showed up in the high LD, indicating that they might be inherited together. This result suggests that this protein may play a role in starch biosynthesis and possibly amylose/amylopectin initiation in rice grains. In the present study, a total of 18 SNP positions on chromosomes 1, 6, and 11 contained the highest significant loci responsible for the starch property . On chro protein . This ma protein A. The he protein , which ie leaves . Moreoveen cells . In planLOC_Os06g04200, which encodes starch synthase or granule-bound starch synthase I (GBSSI). This locus affected AAC, RAG, and RS contents. Previous studies showed a significant correlation between SNPs in the intron 1 (G/T), in the coding regions exon 6 (A/C) and exon 10 (C/T), and the AAC of GBSSI [LOC_Os06g04150, LOC_Os06g04169, and LOC_Os06g04190  on chromosome 11 were in LOC_Os11g01580, which encoded the sodium/calcium exchanger protein (NCX). Interestingly, at this locus, the SNPs associated with % AAC are heterozygous .The rice population used in this study comprised 230 rice varieties  grown at®M3, San Jose, CA, USA). The percentage of apparent amylose content was calculated from the absorbance using the standard curve of amylose from potatoes .The rice samples were dehulled into brown rice. Subsequently, they were ground and sieved through a 100-mesh sieve to measure the AAC, which was determined colorimetrically, measuring the amylose–iodine complex according to the modified method of Juliano . A total20), 120 min (G120), and TG. Aliquots of 50 µL of each solution were incubated with 1.5 mL of GOPOD at 50 °C for 10 min. The absorbance was read at 510 nm against a reagent blank. The starch fractions were calculated as follows:Analyses of digestible starch, including RAG, SAG, total glucose (TG), and RS were performed according to the modified method of Englyst et al. . Thirty o-phosphoric acid at 37 °C and shaking at 200 rpm for 60 min. The solution was mixed with 200 µL of α-amylase (125 mg/mL) before being transferred to a dialysis tube . The dialysis tube was placed in a beaker containing 40 mL of 0.1 M phosphate buffer (pH 6.9) and incubated at 37 °C at 150 rpm for 240 min. Aliquots (0.4 mL) were collected from each sample after 30, 60, 120, 180, and 240 min. A total of 1.2 mL of 0.4 M sodium acetate buffer (pH 4.75) was added to each aliquot, and 24 µL of AMG (3260 U/mL) was added to hydrolyze the digested starch and incubated at 50 °C for 30 min. Duplicate aliquots of 150 µL each of timing solution were incubated with glucose oxidase peroxidase reagent at 50 °C for 20 min, and the absorbance was measured at 510 nm using a microplate reader. Maltose was used as the standard carbohydrate. The HI was calculated by dividing the area under the curve of each sample by that of maltose. HI was converted into a glycemic index using the formula given by Goni et al., as shown in Equation (4) [In vitro methods were used to measure the HI and the GI. The protocol was based on a modified method described by Kumar et al. . A totaltion (4) :GI = 39.https://plants.ensembl.org/index.html, accessed on 8 February 2020) using the Burrows–Wheeler Aligner (BWA version 0.7.1). Variants were called using the Genome Analysis Toolkit . Variants were filtered using the following criteria: minimum coverage was 6 or 3 if positions with the minimum coverage of 6 were found in at least 10 accessions for a position to be called homozygous; and minimum coverage and minimum percentage of each of the two observed major base calls were 5 and 20, respectively, and minimum total coverage was 10 for a position to be called heterozygous. Identification of candidate genes underlying the genetic regulation of phenotypic traits using genome-wide association (GWA) mapping was performed using GEMMA Software (https://github.com/genetics-statistics/GEMMA) [http://rice.plantbiology.msu.edu) was used to search for the corresponding gene to each SNP. The linkage disequilibrium (LD) blocks were used to identify candidate gene regions using the Haploview software 4.2 [Short reads obtained from the Illumina Genome Analyzer  were demultiplexed and mapped onto the rice reference genome sequence downloaded from the Plant Ensemble database (version IRGSP-1.0) (ware 4.2 . The SNPhttps://www.ncbi.nlm.nih.gov/tools/primer-blast/), based on GWAS results. Marker I was located on LOC_O06g04200; RSM1-F: 5′ AGCTTCAAATTCTAATCCCCAA, 3′ RSM2-R, 5′ TTACACATCCATCCAATGCG 3.’ Marker II was located on LOC_Os11g01580; RSM3-F: 5′ AACTTCGTCTCTGCTACGTT 3′, RSM4-R: 5′ CTCGTCG TGCTGTTCTAC 3′. The PCR was performed using a PCR reaction mixture of 20 µL containing 1 μg of DNA template, primers (0.5 μM each of forward and reverse primers), 0.5 μL of 10 mM dNTPs, 2 µL of 10X Taq buffer, 1 μL of 25 mM MgCl2, and 0.2 µL of Taq DNA polymerase . The PCR conditions were performed on a thermal cycler  as follows: an initial denaturation at 95 °C for 3 min; 34 cycles of denaturation at 95 °C for 30 s; annealing at 57.5 °C for primer 1 and 53.8 °C for primer 2 for 30 s and extension at 72 °C for 1 min; and a final extension at 72 °C for 5 min. The PCR product of primer set 1 (marker I) was digested with BpuEI enzyme  according to the manufacturer’s instructions. Next, the amplified PCR products of markers I and II were run on a 1% agarose gel and used to assess the band size with a 100 bp ladder marker . The designed markers were used to detect the AAC variations in two sets of 100 rice cultivars . Pearson correlation analysis was used to determine the relationships among the starch properties, and the correlation plots were constructed using the R “corrplot” package. All results are presented as mean ± standard error of the mean. Hierarchical cluster analysis using the Euclidean distance was used to draw relationships among rice cultivars based on the starch properties, and a dendrogram was generated based on Ward’s method using JMP Software version 9 . Box plots were created using the R ‘ggplot2′ package.An analysis of variance was performed to detect differences among the means of each parameter in five replicates, and Tukey’s range test was used to detect significant differences between each mean at LOC_Os01g65810, LOC_Os06g04200, and LOC_Os11g01580, contained the most significant SNPs and were identified as the determinants of starch properties in rice grains. These loci were used to develop molecular markers that could be used to predict starch properties and improve rice quality.This study showed that the apparent amylose content displayed a strong positive correlation with the resistant starch and a negative correlation with the rapidly available glucose. Furthermore, the rapidly available glucose was negatively correlated with the resistant starch. Using a GWAS, three loci, namely Chadchawan, S.; Buaboocha, T.; Pongpanich, M.; Praphasanobol, P. Rice Starch Marker 1 (RSM1) Rice Starch Marker 2 (RSM2). TH patent 2,203,000,528, 27 February 2022. Chadchawan, S.; Buaboocha, T.; Pongpanich, M.; Praphasanobol, P. Rice Starch Marker 3 (RSM3) Rice Starch Marker 4 (RSM4). TH patent 2,203,000,529, 27 February 2022.