The continued emergence of human coronaviruses (hCoVs) in the last few decades has posed an alarming situation and requires advanced cross-protective strategies against these pandemic viruses. Among these, Middle East Respiratory Syndrome coronavirus (MERS-CoV), Severe Acute Respiratory Syndrome coronavirus (SARS-CoV), and Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) have been highly associated with lethality in humans. Despite the challenges posed by these viruses, it is imperative to develop effective antiviral therapeutics and vaccines for these human-infecting viruses. The proteomic similarity between the receptor-binding domains (RBDs) among the three viral species offers a potential target for advanced cross-protective vaccine designs. In this study, putative immunogenic epitopes including Cytotoxic T Lymphocytes (CTLs), Helper T Lymphocytes (HTLs), and Beta-cells (B-cells) were predicted for each RBD-containing region of the three highly pathogenic hCoVs. This was followed by the structural organization of peptide- and mRNA-based prophylactic vaccine designs. The validated 3D structures of these epitope-based vaccine designs were subjected to molecular docking with human TLR4. Furthermore, the CTL and HTL epitopes were processed for binding with respective human Lymphocytes Antigens (HLAs). In silico cloning designs were obtained for the prophylactic vaccine designs and may be useful in further experimental designs. Additionally, the epitope-based vaccine designs were evaluated for immunogenic activity through immune simulation. Further studies may clarify the safety and efficacy of these prophylactic vaccine designs through experimental testing against these human-pathogenic coronaviruses. Human coronaviruses (hCoVs) form a diverse group of single-stranded RNA viruses . Among tThe proteomic organization of hCoVs includes the spike protein, which has an important structural and functional role. The spike protein mainly plays a key role in mediating the CoV’s interaction with the host cell . Spike pThe proteomic similarity of the three highly pathogenic hCoVs may be utilized to set up precise strategies to develop cross-protective therapeutics against these deadly pathogens ,21,22. AThe spike glycoprotein containing the RBD sequences of MERS-CoV, SARS-CoV, and SARS-CoV-2 was downloaded from UniProt. The accession details of each domain of the three different coronaviruses that were evaluated for further screening for putative T-cell, B-cell, HTL, and IFN epitopes are given in Different epitopes recognized by the immune system were characterized as a prerequisite for peptide-based vaccine designs. Herein, each RBD protein sequence of MERS-CoV, SARS-CoV, and SARS CoV-2 was subjected to putative vaccine epitope screening. This was followed by the selection of T-cell, HTL, and B-cell epitopes for the final prophylactic vaccine designs.An immune response mainly requires T-cell-specific recognition of antigenic epitopes for robust induction. These T-cell-recognized epitopes are vital to combat invading pathogens from developing infections . MoreoveHTL epitopes play a pivotal role in the induction of a protective immune response against invading pathogens . They arB-cell epitope mapping plays a key role in robust immune response induction. This is due to the important role of B-cells in the regulation of an immune response through the production of antibodies against the invading pathogen ,31. ThisPeptides are presented to T-cells by HLA molecules, which results in the T-cell immune response. HLA molecules are highly polymorphic in nature; hence, peptides capable of interacting with HLA molecules are potent vaccine candidates . In siliFor the docking complexes of the CTL epitopes with their corresponding HLAs, the total binding free energies were −27.86 kcal/mol for HLA-B*57:01-MTEQLQMGF, −20.65 kcal/mol for HLA-B*35:01-NATKFPSVY, and −21.87 kcal/mol for HLA-A*01:01-VGGNYNYLY. The CTL–HLA docking complexes are shown in The use of computational vaccinology approaches accelerates the design of protective vaccines with increased safety and efficacy. Advanced trends including the development of predisposed biological resources have been adapted to accelerate vaccine designing against human-pathogenic species ,23,24. TAn mRNA-based vaccine was designed by targeting the RBD of the selected three hCoV species. This comprised joining together the epitopes predicted for the RBDs of all the studied species in the final vaccine construct (MEVC-mRNA-CoV). The final vaccine construct contained four CTL epitopes, four HTL epitopes, and six linear B-cell epitopes. The selected CTL, HTL, and linear B-cell epitopes were placed together using AAY, PMGLP, and GGGGS linkers, respectively. The 5′ m7G cap was placed right before the NCA-7d (5′ UTR) followed by the Kozak sequence and signal peptide (tissue Plasminogen Activator). The mRNA vaccine sequence spanned from the tPA to the last LBL epitope, where a stop codon was added right before the S27a+R3U (3′ UTR) and poly (A) tail (120 nucleotides long). The selected CTL, HTL, and B-cell epitopes are provided in The final vaccine construct containing the putative CTL, HTL, and B-cell epitopes was joined using different linkers, as shown in http://www.jcat.de, accessed on 26 November 2021) was utilized; after providing the input protein sequence, we selected E. coli strain-K12 as the target organism. This was followed by further analyses and choosing the “Avoid rho-independent transcription terminators” and “Avoid prokaryotic ribosome binding sites” options in the online server. The GC content (51.67%), CAI value (0.96), molecular weight (35kd), theoretical pI (9.82), and several other analyzed parameters of MEVC-Prophylactic-CoV are shown in The total length of MEVC-Prophylactic-CoV was 329 amino acids, and it had a high antigenicity score of 0.73 and showed a nonallergenic nature. The final prophylactic vaccine was evaluated for numerous physiochemical properties to ensure the safety and efficacy of the vaccine design. During this process, JCat  strain as represented by the CAI value of the construct. The optimized sequence was added to a Pet28a + vector with the utility of SnapGene software . The specific sites of restriction enzymes selected for insertion of the optimized sequence into the vector were EcoR1 and Xho1. The final plasmid map obtained for MEVC-CoV is shown in Using an immune simulation approach, the potential immune response induction with predicted antibody titers produced against the MEVC-prophylactic-CoV construct was evaluated. As was predicted , the injThe potential interactions of the designed prophylactic vaccine with human immune receptor TLR4 through molecular docking analysis were also explored. Toll-like receptors (TLRs) play a crucial role in the activation of innate immunity by detecting conserved pathogen-associated molecular patterns (PAMPs) and the production of an adaptive immune response against the invading pathogens. Specifically, TLR4 was implicated in the recognition of hCoV structural proteins related to immune response induction. The docking complexes for each of the designed prophylactic MEVCs and TLR4 are shown in https://www.uniprot.org/, accessed on 2 June 2021) [The amino acid sequences of the receptor-binding domains of the three target hCoVs  were downloaded from the Universal Protein Knowledge Base (ne 2021) .Putative epitopes specific to RBD sequence of each studied species were characterized on the basis of the utility of various online available servers. This was followed by immunogenic potential  evaluation of the individually characterized immune epitopes  for each RBD of the studied hCoV species.http://www.cbs.dtu.dk/services/NetCTL/, accessed on 3 June 2021) [The prediction of CTL epitopes for each RBD of the three hCoV species was carried out with NetCTL 1.2 server (ne 2021)  at defaune 2021) .http://www.iedb.org/, accessed on 8 June 2021) [The predictions of HTL epitopes for each RBD of the selected viral species were conducted using IEDB MHC-II module (ne 2021) , correspne 2021) . The binhttps://webs.iiitd.edu.in/raghava/abcpred/ABC_submission.html, accessed on 15 June 2021) server [The prediction of B-cell epitopes for each RBD of the studied hCoV species was performed using ABCpred  server . This seThe epitopes, before inclusion in the final vaccine constructs, were evaluated for immunogenic potential  through using two different servers. Similarly, MHC-I binding potentials of the CTL and HTL epitopes were also evaluated.http://www.ddg-pharmfac.net/vaxijen/VaxiJen/VaxiJen.html/, accessed on 18 June 2021) [VaxiJen server (ne 2021)  was emplhttps://www.ddg-pharmfac.net/AllerTOP/index.html, accessed on 21 June 2021) [Putative vaccine epitopes and final vaccine constructs were subjected to allergenic profiling using AllerTOP 2.0 server (ne 2021) . AllerTOhttp://tools.iedb.org/mhci/, accessed on 29 June 2021) [To predict putative binding MHC class I alleles corresponding to short-listed CTL and HTL epitopes in the final vaccine design, IEDB MHC-I binding tool (ne 2021)  was utilhttps://webs.iiitd.edu.in/raghava/pepstrmod/, accessed on 9 July 2021) tool. Additionally, the HawkDock server  [The HawkDock server was used for in silico molecular docking of selected T-cell and HTL epitopes with respective HLAs. The server was provided with 3D structures (pdb format) of the HLAs as receptors and the epitopes as ligands. Herein, the 3D models of Human Leukocyte Antigens corresponding to selected T-cell and HTL epitopes were downloaded from the Protein Databank (PDB) . Furtherly 2021)  was utilAfter short-listing several putative epitopes with qualifying parameters, the process of vaccine construction took place. This included the construction of an mRNA- and a multiepitope-based prophylactic vaccine against the highly pathogenic hCoVs.The formulation of the mRNA vaccine required the addition of several critical components in the open reading frame of the construct. This included 5′ and 3′ UTRs, Kozak sequence, putative epitopes, suitable linkers, and a stop codon. The start codon (AUG) is included in the Kozak sequence , whereashttps://prosa.Servi-ces.came.sbg.acat/prosa.php, accessed on 21 July 2021) [https://servicesn.mbi.ucla.edu/PROCHECK/, accessed on 23 July 2021), and VERIFY3D . Additionally, the physicochemical properties were evaluated for each of the designed vaccines through Expasy [All the short-listed epitopes for vaccine designs were subjected to the process of multiepitope-based vaccine construction. A total of 3 CTL and 6 HTL and 6 B-cell epitopes were included in the final prophylactic multiepitope vaccine construct against hCoVs. This was achieved through the use of different linkers  to join the epitope sequences in the final constructs. Moreover, adjuvant human beta defensin 2 (HbD-2) was added at the N terminal to improve the stability of the vaccine construct. The finalized MEVC was evaluated for antigenicity and allergenicity status. Furthermore, the 3D structures for the designed MEVC (multiepitope vaccine construct) were modeled through using Robetta server. The structures were also validated through the use of ProSA-web (ly 2021) , PROCHECh Expasy .http://cadd.zju.edu.cn/hawkdock/, accessed on 10 August 2021) was utilized to calculate the MM/GBSA score for the best docking poses.The designed prophylactic-MEVC-CoV was processed for molecular docking analysis with human TLR4 using the HawkDock server. The server is a multifunctional program that integrates the HawkRank scoring function, the ATTRACT algorithm for docking, and the MM/GBSA free energy decomposition analysis. The molecular mechanical energies and scale solvent methods are combined in the MM/GBSA method . MoreoveXho1 and EcoR1) sites were selected to perform in silico cloning and acquire the final plasmid map.The optimized DNA sequence for the final prophylactic-MEVC-CoV design was obtained through the utility of JCat server. The CAI values and GC content for the final vaccine construct sequence were also obtained. The DNA sequence was then inserted into a protein expression plasmid vector (Pet28a+) through using SnapGene. Restriction enzyme (In silico immune simulation was carried out to predict the real-life immune system response to the prophylactic-MEVC-CoV design using C-ImmSim server . This siIn conclusion, an immunoinformatics approach was utilized to short-list putative immune epitopes for RBDs that were utilized in the final construction of an MEVC for three different hCoVs. Different epitopes, including T-cell, B-cell, and HTL epitopes, were mapped and included in the final vaccine design against the target RBDs. Two different prophylactic vaccines were designed including an mRNA-based vaccine and a proteome-wide multiepitope-based vaccine. Additionally, the short-listed T-cell and HTL epitopes were analyzed through molecular docking with their corresponding HLAs, which demonstrated high binding energies. This step was followed by structural modeling and the evaluation of the potential immunization ability of the constructed MEVC against hCoVs. This study provides new insights into the development of future vaccines against pandemic coronaviruses. The characterization of epitopes and MEVC designs with their evaluated immunogenic potential suggested that experimental processing of these vaccines against hCoVs is needed. However, further experiments may validate the immune reinforcement potential of these putative vaccine candidates.


UC + ANN treatments presented pasting temperature, gelatinization temperature and crystallinity increased whereas the glycemic index of both rice samples was decreased as compared to its native. Especially, UC + ANN55 treated rice produced the lowest glycemic index and starch hydrolysis. Moreover, UC + ANN treated CN1 rice exhibited delayed gelatinization temperature, increased gelatinization enthalpy, and decreased glycemic index than KDML105 rice. In addition, Pearson’s correlation presented that UC + ANN and amylose content had a highly negative correlation with the glycemic index at p < 0.0.1. The result exhibited that UC followed by ANN show an effective way to modify starch granules with delayed starch hydrolysis reduced glycemic index and properties depending on annealing temperature and rice cultivar.White rice samples, Chai-Nat1 (CN1) and Jasmin rice (KDML105), were treated with the ultrasound-chilling (UC) and combined with annealing treatments . Their physicochemical properties and  Rice grain is an excellent natural carbohydrate source that serves in daily meals, especially in Asian countries Rice  and Chai-Nat1 rice grains (CN1 cultivar with 29.35% amylose content), were obtained from Upon Ratchathani province, Thailand. The crude protein, fat, and total starch of KDML105 rice were 7.29, 0.89, and 73.76% (w/w), respectively, while CN1 has a corresponding crude protein, fat, and total starch of 7.39, 1.25, and 71.44% (w/w). α-amylase , amyl glucosidase  and glucose oxidase peroxidase (GOPOD) kit were obtained from Megazyme International, Ireland Ltd. Other chemicals used were analytical grade.2.23). The aluminum basket was immersed in a 6L of water in ultrasonic bath  with an amplitude at 70%, at a frequency of 60 kHz, and power 665 W at the temperature range from 30 to 35 °C for 15 min. The ultrasound treated rice grains samples were removed and drained for 1 min. The drained rice grains were then stored in sealed polyethylene film bags at 4 °C for 24 h and subsequently dried at 40 ± 5 °C for 5 h to obtain a final moisture content of 11%. The dried rice grains were gently ground as a powder, sieved using a 160 µm sieve. All samples were stored in a desiccator until analysis.Rice grains (500 g) samples were put in an aluminum basket  . Rice powder  was add 25 mL of distilled water in a RVA canister. RVA settings were performed as follow: heating from 50 to 95 °C for 3.42 min, holding at 95 °C for 2.3 min and cooling to 50 °C for 3 min. The initial stirred for thorough dispersion at 960 rpm for 10 s and then followed by 160 rpm during analysis. Pasting parameters were recorded using Thermocline software for Windows.2.5−1. The onset temperature (To), peak temperature (Tp), conclusion temperature (Tc), gelatinization temperature range (Tc-To) and gelatinization enthalpy (ΔH) were evaluated from DSC curve by STARe software .The thermal properties of rice powder samples were analyzed using a differential scanning calorimeter (DSC) . The rice powder sample  was mixed with deionized water (9 µL) in an aluminum pan. The sample pan was hermetically sealed and equilibrated for 24 h at room temperature before analysis. An empty pan was used as a reference. The temperature profile was set from 20 to 120 °C with heating rate of 5 °C min2.6−1. The relative crystallinity (%) was calculated as described by Rewthong, Soponronnarit The XRD pattern of rice samples were analyzed by an X-ray diffractometer . The samples were determined at scanning in the 2Θ ranged from 5° to 40°, with a target voltage of 40 kV and 40 mA and scan speed of 2° min2.7in vitro glycemic index was analyzed by modified AACC method 32–40.01 . The rice grains sample was ground using a grinder  and passed through a 1.0 mm sieve. The ground rice (100 ± 5 mg) was incubated in 4 mL of 0.1 M sodium maleate buffer (pH 6) with 10 mg/ml of pancreatic α-amylase and 3.3 U/mL of amyloglucosidase at 37 °C for 0, 30, 60, 120, 150 and 180 min in a shaking water bath (200 S/min). Hydrolyzed rice sample was added 99 %v/v of ethanol and centrifuged at 1500 g for 10 min at room temperature. The supernatant was decanted into 100 mL of volumetric flask. The pellet was washed by adding 8 mL of 50%v/v of ethanol and sample was centrifuged again at 1500 g for 10 min. The supernatant was decanted into the initial supernatant and the volume adjusted by adding sodium acetate buffer (pH 4.5) to 100 mL in a volumetric flask. Then, 0.1 mL of this aliquot was collected into a test tube with 10 µL of 300 U/mL of dilute amyloglucosidase solution in 100 mM of sodium maleate buffer (pH 6.0). The sample was incubated in a water bath at 50 °C for 20 min. The glucose content was estimated using glucose oxidase peroxidase (GOPOD) kit. The kinetics of hydrolysis was calculated from a non-linear first-order equation C = C∞ (1 – e-kt), which was established by Goñi et al. ∞, and k is the percentage of starch hydrolyzed at time t (min), the equilibrium percentage of starch hydrolyzed after 180 min, and the kinetic constant, respectively. The hydrolysis index (HI) of rice samples was calculated by dividing the area under the hydrolysis curve (AUC) of samples by AUC of a reference as white bread . The AUC was calculated by the equation: AUC = C∞ (tf - t0) – (C∞ / k) (1 – e (-k (tf - t0))), where tf is the final time and t0 is the initial time. The eGI was calculated using the equation: eGI = 39.71+(0.549HI) as described by Goñi et al. The 2.8p < 0.05.The data were reported as mean ± standard by triplicate measurement. The data were analyzed for analysis of variance (ANOVA), followed by Comparison of mean using Duncan’s multiple range tests at 5% significance level. Statistical analysis was performed by using the SPSS 22.0 statistical software program . Pearson’s correlation coefficient was determined the relation of parameters at a significance level of 33.1p < 0.05) in all pasting profiles because of their amylose content. The pasting temperature of KDML105 and CN1 rice was increased with UC treated rice. This result might be due to ultrasound waves could first and more important stage to produce the higher percent of short-chain starch facilitating for retrogradation process to form into perfect crystallite and more organized configuration of double helices The pasting properties are associated with the viscosity of starch gel, the stability of starch paste, and retrogradation tendency For CN1 rice, the peak viscosity value was not change after UC treated rice grains. This result was agreed with our previous study The breakdown value is related to the thermal stability of swollen starch granules during heating and shearing force. The decrease in breakdown indicates that more stable structure of starch granules to shear force 3.2The thermal properties of rice samples were evaluated from the parameters, including onset temperature (To), peak temperature (Tp), conclusion temperature (Tc), temperature range (Tc-To) and enthalpy change (ΔH) as measured by DSC are summarized in The gelatinization temperatures of natives for KDML105 and CN1 presented in the temperature range of 61.81 to 77.69 °C and 71.62 to 81.86 °C and enthalpy change was 1.63 and 1.12 J/g, respectively. The difference in the gelatinization temperatures and enthalpy change of both rice cultivars correlated with their amylose content which was explained by Varavinit et al. For all UC + ANN samples, the increased gelatinization temperatures  through annealing treatment taking after UC treatment. These results agreed with increase in pasting temperature as presented in 3.3The X-ray diffraction pattern and relative crystallinity of native, UC and UC + ANN samples for KDML105 and CN1 rice are shown in 3.4in vitro glycemic index are shown in ∞) of rice samples are shown in The results of However, when ANN treatments plus after UC treatment of both rice cultivars were significantly decreased kinetic constant by decreased from 17.13 to 13.73 for KDML105 cultivar and 12.93 to 9.37 for CN1 cultivar. These results showed that annealing treatment promotes the reorganization of starch granules attributing to an increase in granular stability. Ultrasound treatment resulted in forces by waves that affected the starch granules and cut long chains into appropriate lengths. Then, ANN treatment could be promoted further ordered rearrangement of debranched starch chains to form highly ordered crystalline structure and may be led to the formation of SDS and RS, especially RS3 type The hydrolysis index (HI) is used for estimating the glycemic index of carbohydrate foods The expected glycemic index (eGI) of natives, UC and UC + ANN treated rice sample for both rice cultivars are presented in 3.5in vitro glycemic index of KDML 105 and CN1 cultivar are shown in Pearson correlation coefficients among physicochemical variables and in vitro glycemic index variables which included k, HI, and eGI . High amylose contents are not only more suitable for producing resistant starch but also presents lower hydrolysis by α-amylase attributing to the major component of amylose being crystalline which reflects the compact structure of starch granules It was supported by the studies of Wang et al. 4in vitro digestion characteristic were significantly changed by UC and UC + ANN treatments. The UC treatment of both rice cultivars was proposed to enhance the re-ordering of starch molecules which were responsible for changing their physicochemical properties, including pasting properties, thermal properties, relative crystallinity. Moreover, ultrasound-chilling following by annealing treatment for both rice cultivars resulted in a significantly promote a large change in pasting temperature, gelatinization temperatures, and in vitro glycemic index characteristics. The higher temperatures of annealing treatment promoted the arrangement of amorphous region within starch granules, especially annealing at 55 °C treated rice with higher amylose content presented significantly lowest glycemic index (eGI). Ultrasound-chilling with ANN treatment could be used as a technology for modifying rice grains and/or starch to improve resistant enzymatic hydrolyses and resistant starch and produce low glycemic index rice product in further.The pasting properties, thermal properties, relative crystallinity, and Kannika Kunyanee: Investigation, Methodology, Validation, Formal analysis. Tai Van Ngo: . Sandra Kusumawardani: . Naphatrapi Lungsakul: Conceptualization, Validation, Supervision, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


MSTN) regulates muscle development and body metabolism through a variety of pathways and is a core target gene for gene editing in livestock. Gut fungi constitute a small part of the gut microbiome and are important to host health and metabolism. The influence of MSTN mutations on bovine gut fungi remains unknown. In this study, Internal Transcribed Spacer (ITS) high-throughput sequencing was conducted to explore the composition of gut fungi in the MSTN mutant (MT) and wild-type (WT) cattle, and 5,861 operational taxonomic units (OTUs) were detected and classified into 16 phyla and 802 genera. The results of the alpha diversity analysis indicated that no notable divergence was displayed between the WT and MT cattle; however, significant differences were noticed in the composition of fungal communities. Eight phyla and 18 genera were detected. According to the prediction of fungal function, saprotroph fungi were significantly more abundant in the MT group. The correlation analysis between gut fungal and bacterial communities revealed that MSTN mutations directly changed the gut fungal composition and, at the same time, influenced some fungi and bacteria by indirectly regulating the interaction between microorganisms, which affected the host metabolism further. This study analyzed the role of MSTN mutations in regulating the host metabolism of intestinal fungi and provided a theoretical basis for the relationship between MSTN and gut fungi.Myostatin ( The gut microbiota consists of microbial cells, including bacteria, fungi, archaea, protozoa, and viruses, and their relevant genetic material exists in the gastrointestinal (GI) tract of the host . AlthougMSTN) is an important negative regulatory factor in skeletal muscle development  technology have more developed muscles and a higher lean meat yield  and wild-type (WT) cattle were investigated, and the association between gut fungal composition and bacterial composition was analyzed. These findings are of great significance to the metabolism-regulating mechanisms of MSTN and the interactions between bacteria and fungi.In this study, the gut fungal compositions of the This study was performed according to animal care and ethics in China and was approved by the Animal Ethics and Welfare Committee (AEWC) of Inner Mongolia University and Baotou Teacher's College.The cattle used in the present study were presumably 24-month-old females raised as common beef cattle in a local ranch. Sample collection was performed as described in an earlier study . Total DA total of 492,564  raw sequences of the ITS2 regions in the six sequenced samples (three MT and three WT cattle), with 35,429–102,161  non-chimeric reads and 5,861 OTUs based on 97% sequence similarity, were obtained. These data were classified into 16 phyla, 64 classes, 159 orders, 372 families, and 802 genera. Both the Shannon diversity index and rarefaction curve tended to flatten, demonstrating that the depth and quantity of the sequencing met the requirements for further analysis , B.In total, the number of OTUs from the gut fungi shared by cattle in both the MT and WT groups was 428, while 2,869 and 2,563 OTUs existed only in the MT and WT cattle , respectP = 0.65), Simpson , ACE , and Chao1  indices between the MT and WT cattle (P > 0.05)  –D, suggeThe divergence in the fungal composition between the WT and MT cattle was assessed using the NMDS based on binary Jaccard distances to generate scatter plots  and the The relative abundance of dominant fungi at the phylum and genus levels was analyzed, and visible changes in the fungal composition were noticed between the WT and MT cattle. Among the 16 phyla identified in the six fecal samples, the average relative abundance of eight phyla was >1% , includiCaecomyces (5.02%), Mortierella (3.53%), unclassified_Ascomycota (3.24%), Alternaria (3.21%), Comoclathris (2.94%), Fusarium (2.76%), unclassified_Basidiomycota (2.53%), Orpinomyces (2.42%), Debaryomyces (2.42%), Cladosporium (2.35%), Penicillium (2.14%), unclassified_Sordariomycetes (2.11%), Cyllamyces (1.99%), Piromyces (1.74%), Thermomyces (1.59%), Filobasidium (1.52%), and Thermoascus (1.05%). Unclassified_fungi (9.31%) was the most predominant genus in the MT group, followed by Mortierella (4.20%) and Alternaria (4.10%), which together comprised ~50.74% of the total taxonomic groups identified. Caecomyces (7.85%), unclassified_fungi (5.50%), and Orpinomyces (4.08%) were the three most predominant genera in the WT group, comprising about 49.23% of the total taxonomic groups identified.A total of 802 genera were detected, 18 of which had an average relative abundance of more than 1% , includiMSTN mutations interfere with the gut fungal communities of the cattle, and a relative abundance of more than 1% was determined at different classification levels (P < 0.05)   . At the  < 0.05) . ConsideN mutant , C. The P > 0.05), and saprotroph fungi were markedly more abundant in the MT group compared to those in the WT group, while the relative abundance of symbiotrophs was less in the MT group compared to that in the WT group, and no significant differences were observed   .P < 0.05) , animal endosymbionts (22.72 ± 9.24%), and plant saprotrophs (17.88 ± 6.84%). The most important functions in the MT group were undefined saprotrophs (27.41 ± 9.61%), animal endosymbionts (16.12 ± 3.12%), and plant saprotrophs (12.57 ± 2.64%). The most important functions in the WT group were animal endosymbionts (29.33 ± 8.54%), plant saprotrophs (23.20 ± 5.01%), and undefined saprotrophs (19.55 ± 5.66%), sequentially. The functions that were significantly different between the MT and WT groups were undefined saprotrophs and plant saprotrophs ( < 0.05) .Agaricales was negatively correlated with Comoclathris (-1) and Naganishia (−0.8857) but positively correlated with Saccharomyces (0.8286). Tausonia was negatively correlated with Penicillium (-1) and positively correlated with Caecomyces (0.8286), Cyllamyces (0.9429), and unclassified_Leotiomycetes (0.9429). Talaromyces was negatively correlated with Filobasidium (-1), Naganishia (−0.9429), Thermomyces (−0.8857), and Vishniacozyma (−0.8857) and positively correlated with Saccharomyces (0.8286) and Inocybe (0.8286). Chaetomium was negatively correlated with Inocybe (-1), Debaryomyces (−0.9429), and Talaromyces (−0.8286) and positively correlated with Thermomyces (0.8286), Filobasidium (0.8286), and Thermoascus (0.8857). Caecomyces was negatively correlated with unclassified_fungi (−0.9429). Penicillifer was negatively correlated with Caecomyces (−0.9429) and positively correlated with unclassified_fungi (0.8857) and unclassified_Sordariomycetes (0.9429). Alternaria was negatively correlated with unclassified_Ascomycota (−0.9429). Cyllamyces was negatively correlated with Penicillium (−0.9429) and unclassified_fungi (−0.8286). Unclassified_Leotiomycetes was negatively correlated with Penicillium (−0.9429) and positively correlated with Cyllamyces (0.8286). Saccharomyces was negatively correlated with Thermomyces (−0.9429), Comoclathris (−0.8286), Piromyces (−0.8286), and Filobasidium (−0.8286) and positively correlated with unclassified_Ascomycota (0.8286). Limnoperdon was negatively correlated with Caecomyces (−0.9411), Tausonia (−0.9411), Cyllamyces (−0.8804), and unclassified_Leotiomycetes (−0.8804) and positively correlated with unclassified_fungi (0.8804), unclassified_Sordariomycet (0.8804), Penicillium (0.9411), Penicillifer (0.9411), and Helicobasidium (0.9411). Unclassified_Sordariomycetes was negatively correlated with Caecomyces (−0.8857) and positively correlated with Mortierella (0.8857) and unclassified_fungi (0.9429). Archaeorhizomyces was negatively correlated with Fusarium (−0.8857). Piromyces was negatively correlated with Debaryomyces (−0.8857). Inocybe was negatively correlated with Thermoascus (−0.8857), Thermomyces (−0.8286), and Filobasidium (−0.8286) and positively correlated with Debaryomyces (0.9429). Naganishia was negatively correlated with Saccharomyces (−0.8857) and positively correlated with Comoclathris (0.8857), Thermomyces (0.9429), and Filobasidium (0.9429). Mortierella was negatively correlated with Caecomyces (−0.8286) and positively correlated with unclassified_fungi (0.9429). Penicillium and Thermomyces were negatively correlated with Caecomyces (−0.8286). Helicobasidium was negatively correlated with Caecomyces (−0.8286) and Tausonia (−0.8286) and positively correlated with Penicillium (0.8286), unclassified_Sordariomycetes (0.8857), and Penicillifer (0.9429). Saitozyma was negatively correlated with Comoclathris (−0.8286) and positively correlated with unclassified_Agaricales (0.8286). Orpinomyces was negatively correlated with unclassified_Basidiomycota (−0.8286). Thermoascus and Plectosphaerella were negatively correlated with Debaryomyces (−0.8286). Plectosphaerella was positively correlated with Piromyces (0.8857). Trichoderma was negatively correlated with Piromyces (−0.8286) and positively correlated with unclassified_Ascomycota (0.8857) and Saccharomyces (0.8857). Filobasidium was positively correlated with Thermomyces (0.8857). Vishniacozyma was positively correlated with Filobasidium (0.8857), and Cladosporium was positively correlated with unclassified_Basidiomycota (0.9429).The correlations between the different gut fungi were analyzed using the network analysis . The resBacteroidetes (P < 0.05), and Rozellomycota and Mortierellomycota were negatively correlated with Firmicutes (P < 0.05).The correlation between fungal and bacterial species was analyzed to distinguish the marker species linking fungi and bacteria. Spearman's correlation between multiple fungal species (>1% in each group) and bacterial species (>1% in each group) of the WT and MT cattle was determined. At the phylum level , MortierFilobasidium and Alternaria were positively correlated with uncultured_bacterium_o_Clostridiales (P < 0.05). Unclassified_Basidiomycota was positively correlated with uncultured_bacterium_o_Mollicutes_RF39 (P < 0.05). Comoclathris and Alternaria were positively correlated with Alistipes (P < 0.05). Debaryomyces was positively correlated with uncultured_bacterium_f_Lachnospiraceae (P < 0.05). Thermomyces and Piromyces were positively correlated with Prevotellaceae_UCG-003 (P < 0.05). Caecomyces and Orpinomyces were positively correlated with Clostridium_sensu_stricto_1 (P < 0.05). Alternaria, Comoclathris, and Filobasidium were positively correlated with [Eubacterium]_coprostanoligenes_group (P < 0.05). Cyllamyces and Orpinomyces were positively correlated with Ruminococcaceae_UCG-009 (P < 0.05). Unclassified_fungi, Penicillium, and unclassified_Sordariomycetes were positively correlated with Rikenellaceae_RC9_gut_group (P < 0.05). Debaryomyces was positively correlated with dgA-11_gut_group (P < 0.05). Fusarium was negatively correlated with Ruminococcaceae_UCG-010 (P < 0.05). Piromyces was negatively correlated with Bacteroides (P < 0.05). Penicillium and unclassified_fungi were negatively correlated with Ruminococcaceae_UCG-009 (P < 0.05), Mortierella was negatively correlated with uncultured_bacterium_f_Ruminococcaceae (P < 0.05), Fusarium was negatively correlated with uncultured_bacterium_o_Clostridiales (P < 0.05), Mortierella and unclassified_fungi were negatively correlated with Clostridium_sensu_stricto_1 (P < 0.05), and Debaryomyces was negatively correlated with Prevotellaceae_UCG-003 (P < 0.05).At the genus level , FilobasMSTN is a negative regulator of muscle development in several species . All Alternaria, unclassified_Sordariomycetes, and Penicillium belong to the phylum Ascomycota, which are associated with improved animal production traits  and wild-type (WT) cattle in this study. The gut fungi analysis revealed that the MSTN mutant had no effect on the richness and diversity of gut fungi. However, compared to wild type (WT) cattle, the composition of gut fungal community was changed in MSTN gene mutant (MT) cattle. According to the prediction of fungal function, the abundance of saprotroph fungi was significantly higher in the MT group compared to that in the WT group. The correlation analysis between the gut fungal and bacterial communities revealed that the MSTN mutation directly changed the gut fungal compositions and indirectly influenced some fungi and bacteria by regulating the interaction of microorganisms, which affect the host metabolism further. This study also analyzed the role of MSTN mutation in regulating the host metabolism of intestinal fungi and provided a theoretical basis for the relationship between the MSTN and gut fungi.The ITS gene high-throughput sequencing was conducted to explore the fungal community composition in the fecal samples of the The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The animal study was reviewed and approved by Animal Ethics and Welfare Committee (AEWC) of Inner Mongolia University and Baotou Teachers College. Written informed consent was obtained from the owners for the participation of their animals in this study.LG and TW designed the research and wrote the manuscript. SW, LW, ZL, and MY collected the samples. LG and LY analyzed the data. GL and LY revised the manuscript. All authors contributed to the article and approved the submitted version.


Background: The relationship between stiffness and drop jump performance in athletes in various stages of development has yet to be fully investigated. The first aim of this study was to investigate the association between the stiffness of the patellar and quadriceps tendon , gastrocnemius–Achilles tendon unit (GAT), and rectus femoris (RF) using drop jump (DJ) performance in young basketball players. The second aim was to investigate possible variations in the stiffness levels of those tissues in different developmental stages. Methods: The stiffness levels of the GAT, PT, QT, and RF were measured in both limbs in 73 male basketball players aged 12 to 18 years. The reactive strength index (RSI), contact time (CT) and jump height (JH) during 30 and 40 cm DJs were also measured. Results: Pearson correlation coefficients showed a significant association between DJ performance and PT, QT, GAT, and RF dynamic stiffness. Moreover, the youngest subjects were found to have lower stiffness values than the older ones. Conclusions: Tissue stiffness can affect athletic performance by modifying the stretch-shortening cycle in young basketball players. Stiffness of muscles and tendons increases during the maturation process. Further investigations could shed light on the effect of training on the stiffness of muscles and tendons. Currently, more than 450 million people in the world play basketball, making it among the five most popular sports on the planet . JumpingAmong the most commonly used tests to assess vertical jump capacity, the drop jump is linked to the fastest stretch-shortening cycle , since tPrevious studies have shown that tendon stiffness increases during maturation, mainly because of muscle growth and a higher mechanical load due to larger body mass . MersmanHowever, to date, there is still a lack of studies investigating muscle and tendon stiffness in a wider age range, as well as their direct relationship with sporting performance during the maturation process. Specifically, young players in team sports in which jumping abilities are very important could be a good model to study the relationship between jump mechanical load and muscle and tendon tissue adaptations from early to late adolescence ,24.In recent years, technological developments have made it possible to create instruments capable of accurately and non-invasively measuring and analyzing the viscoelastometric parameters of tendons and muscles in subjects with pathologies, healthy subjects and athletic subjects ,26,27. RThe relationship between MyotonPRO measurements and drop jump performance in athletes during various developmental stages has yet to be investigated. Therefore, the first aim of the present transversal study was to investigate potential relationships between lower limb tendon and muscle stiffness and the performance parameters of 30- and 40-cm drop jumps (DJ30 and DJ40). The second aim was to investigate possible variations in the stiffness levels of these tissues in different developmental stages of the maturation process in basketball players aged 12 to 18 years.The a priori power analysis was performed using G*Power software (version 3.1.9.4) considering a two way ANOVA test: with an effect size f = 0.4, α = 0.05 and statistical power (1−β) = 0.85, a total of 72 subjects must be enrolled. Given this outcome, seventy-three healthy young males, aged 12 to 18 years, took part in the study. In order to analyze variations in stiffness across developmental stages, the subjects were divided into three age groups . To identify the age at peak height velocity (APHV), the maturity offset (MO) of the three groups was estimated using the alternative model for boys by Moore et al. (2015) . The subThe assessment of stiffness was performed after a warm-up consisting of 5 min of pedaling at moderate intensity, followed by 5 min of squat jump exercises and dynamic stretching. The dynamic stiffness of the gastrocnemius–Achilles tendon unit (GAT), the Patellar Tendon (PT), the Quadriceps Tendon (QT) and the Rectus Femoris (RF) was measured on the left and right sides using the MyotonPro device. For GAT assessment, participants were in a prone position on a massage table with their knees extended and feet hanging barefoot and unsupported from the edge of the table. The measurement points were at 8, 9, 10, 11, 12, 16, and 20 cm from the plantar aspect of the heel, as shown in Drop jump performance was assessed immediately after the stiffness measurements were taken using an optoelectronic system called Optojump . This device consists of a transmitting and receiving bar, each having 96 LEDs. Continuous communication takes place between the LEDs on the transmitting and receiving bars, and the mechanism determines the duration of any communication interruptions between them. This allows for 1/1000-s precise measurement of flight and contact times throughout the execution of a sequence of jumps. Participants performed three DJ30s and three DJ40s with their hands placed on their hips, with 30 s of passive rest between jumps. The subjects were instructed to keep their leg muscles as stiff as possible, trying to minimize joint flexions during landing. The main goal of the DJ was to minimize landing contact time while trying to reach the maximum jump height. Data on contact time (CT30 and CT40), jump height (JH30 and JH40) and the RSI (RSI30 and RSI40) of each drop jump were then calculated and the best DJ30 and DJ40 (defined as the highest jump) were selected for subsequent analysis. Specifically, the RSI is an index of reactive strength derived from the ratio of jump height to ground contact time. It makes it possible to assess the subject’s ability to change rapidly from eccentric to concentric muscle contraction as well as his or her explosiveness during the drop jump .All data were checked for normality using the Shapiro–Wilk test and for equality of variances using Levene’s test. A Pearson’s correlation (rP) analysis was then used to calculate the relationship between drop jump performance variables and muscle or tendon stiffness values. The alpha level was initially set to 0.05, but was then corrected to 0.0236 using the Benjamini-Hochberg procedure to decrease the false discovery rate (FDR < 5%). The effect sizes of rP were established as described by Hopkins and defined as trivial (r = 0–0.1), small (r = 0.1–0.3), moderate (r = 0.3–0.5), large (r = 0.5–0.7), very large (r = 0.7–0.9) and nearly perfect (r = 0.9–1.0) . GAT datDrop jump performance parameters of all participants and age groups are shown in When considering the whole group of participants, a Pearson’s correlation analysis showed that RSI and JH were related to the stiffness of PT, RF and GAT at 8, 9, 10 and 12 cm in both the right and left limbs. In particular, DJ40 showed a larger effect on performance parameters than DJ30, as shown in p < 0.001), as shown in p < 0.001), except for 8–10 cm (p = 0.012) and 9–11 cm (p = 0.067).The analysis of GAT stiffness among the age groups showed a significant increase from 12 to 18 years for both left and right limbs, with all age groups found to be significantly different from each other (p < 0.001), while no difference was found between the 14–15 and ≥16 age groups (p = 0.069). For PT, the tendon stiffness of the ≤13 group was lower than that of the 14–15 (p = 0.028) and ≥16 groups (p = 0.018), while no difference was found between the 14–15 and ≥16 age groups (p = 1.000). The same result was found for QT, with the tendon stiffness of the ≤13 group found to be lower than that of the 14–15 (p = 0.009) and ≥16 groups (p = 0.002), while no difference was found between the 14–15 and ≥16 age groups (p = 1.000).Similarly, for both left and right limbs, RF, PT and QT stiffness showed a significant increase from 12 to 18 years . In partNo differences were found between the left and right limbs for any stiffness parameter or GAT measurement points.The first aim of this study was to investigate the potential relationships between lower extremity tendon and muscle stiffness with the performance parameters of DJ30 and DJ40. First, as expected, jump height and RSI were found to improve from ages 12 to 18 years, while contact time did not change for DJ30 and DJ40, except for a decreasing trend in the ≥16 age group see . After aThese results are in line with a recent investigation by Ando, et al. 2021) [ [21], whRegarding the RF, PT and QT stiffness, this is the first study to investigate and find a significant correlation with RSI and JH . Not surFinally, it is worth mentioning the study by Konrad et al. (2022) , who foup = 0.069) from 14–15 to ≥16 years, indicating that the growth of muscle stiffness probably continues more consistently than PT and QT over the developmental stages examined in the present study  , in all tudy see . In accoA limitation of this study is that MO was assessed using the alternative model for boys by Moore et al. (2015) , which uThis study showed for the first time that muscle and tendon stiffness measured with MyotonPro is related to drop jump performance in young male basketball players. The stiffness of GAT, RF, PT and QT increase with age during adolescence, but the effects of training are still unclear. Further investigation is warranted to clarify the effects of training on muscle and tendon stiffness and the implications of stiffness for injury risk and incidence in young athletes.


To evaluate the outcome of Descemet Membrane Endothelial Keratoplasty (DMEK) in eyes with pre-existing glaucoma.In this retrospective, observational case series we included data of 150 consecutive DMEKs in eyes with pre-existing glaucoma of 150 patients after excluding data of the second treated eye of each patient and of re-DMEKs during follow-up. Cumulative incidences of IOP elevation , post-DMEK glaucoma , graft rejection, and graft failure rate were analyzed using Kaplan–Meier survival analysis. COX regression analysis was used to evaluate independent risk factors.n = 16) compared to Fuchs’ dystrophy (n = 74)  and pre-existing filtrating surgery via glaucoma drainage device (GDD) (n = 10) compared to no surgery/iridectomy (n = 109) .The 36-month cumulative incidence of IOP elevation was 53.5% [95 CI 43.5–63.5%] and of post-DMEK glaucoma 36.3% [95 CI 26.3–46.3%]. Graft rejection occurred with a 36-month cumulative incidence of 9.2% [CI 95% 2.3–16.1]. None of the analyzed risk factors increased the risk for the development of graft rejection. The 36-month cumulative incidence of graft failure was 16.6% [CI 95% 8.4–24.8]. Independent risk factors for graft failure were the indication for DMEK “status after graft failure”  in patients with pre-existing glaucoma are high. In particular, pre-existing filtrating surgery via GDD implantation—but not trabeculectomy—and DMEK after graft failure increase the risk of graft failure. Descemet membrane endothelial keratoplasty (DMEK) has been established as effective treatment for corneal endothelial disorders such as Fuchs’ corneal endothelial dystrophy and bullous keratopathy after cataract surgery. Compared to perforating keratoplasty and Descemet stripping (automated) endothelial keratoplasty (DS(A)EK), DMEK shows excellent clinical outcome, low complication rate, and a rapid visual rehabilitation –4. Due tThe important influence of pre-existing glaucoma on corneal graft survival is known for procedures as perforating keratoplasty and DS(A)EK. Reinhard et al. showed that eyes with pre-existing glaucoma had a significant lower 3-year graft survival rate than eyes without glaucoma after perforating keratoplasty (71% versus 89%) . AdditioA negative influence of postoperative intraocular pressure (IOP) elevation on the graft endothelium is discussed as one of the main reasons, in addition to effects of anti-glaucomatous medication, mechanical damage caused by glaucoma drainage devices (GDD) or need for additional surgeries . As showTherefore, we investigated the long-term postoperative complications including the cumulative incidences of postoperative IOP elevation, post-DMEK glaucoma, graft rejection, and graft failure after DMEK in patients with pre-existing glaucoma over a 36-month time period. Additionally, we analyzed independent risk factors of graft rejection and graft failure.In this retrospective, cohort, outcome study between September 2011 and July 2019, we included eyes from patients with pre-existing glaucoma who underwent DMEK surgery at the Department of Ophthalmology, Charité – Universitätsmedizin Berlin consecutively. Data of the second treated eye of each patient and of re-DMEKs during follow-up were excluded. Ethic approval had been given by the Ethikkommission, Charité – Universitätsmedizin Berlin, EA4/167/16. For this retrospective, single-center study formal consent was not required; the Ethikkommission, Charité – Universitätsmedizin Berlin approved the waiver of consent. The study adhered to the ethical standards of the Declaration of Helsinki. An informed written consent was provided for surgery.Clinical examinations were performed preoperatively and postoperatively after 4 weeks, 3, 6, 12, 24, 36, 48, and 60 months after DMEK. Data after graft failure were not included in the study. The examinations included: best corrected visual acuity (BCVA) tested with a Snellen chart, slit lamp examination, single IOP measurement using Goldmann’s applanation tonometry , endothelial cell density (ECD) measured by specular microscope Nidek CEM-530 (NIDEK Co. Ltd. Japan) and fundoscopy. The Snellen decimal number was converted into logMAR visual acuity using a conversion table. Corneal thickness was not considered for IOP measurement according to results of Maier et al. . In addiGlaucoma diagnosis and classification was based on the criteria of the European Glaucoma Society and definition of pre-existing glaucoma included following criteria: documented history of glaucoma, prior glaucoma filtration surgery, preoperative use of glaucoma medication, and typical glaucomatous excavation of the optic disc or Cup/Disc ratio ≥ 0.6 (categorized by two independent observers). For COX regression analysis, preoperative glaucoma surgery was categorized in four groups: group 1: no previous glaucoma surgery or iridotomy/iridectomy, group 2: IOP lowering filtrating surgery via GDD implantation , group 3: IOP lowering filtrating surgery via trabeculectomy, group 4: all other glaucoma surgeries and glaucoma classification was categorized in four groups: group 1: primary open-angle glaucoma/high pressure glaucoma and normal-pressure glaucoma, group 2: primary angle-closure glaucoma, group 3: (pseudo)exfoliation glaucoma, group 4: all others including pigmentary glaucoma, aphakic glaucoma, uveitic glaucoma, other secondary glaucoma or congenital glaucoma.IOP elevation after DMEK was defined as described by Maier et al.  as IOP ≥Occurrence of keratitic precipitates with corneal edema with or without cells in the anterior chamber or with or without ciliary injection defined graft rejection. An irreversible corneal cloudiness or edema  defined graft failure. The time at which the patient presented with the findings at our clinic was defined as the time of graft rejection or graft failure. An early graft failure was defined as a persisting edema after surgery despite additional air injections (re-bubbling) within 12 weeks after surgery.2 were transplanted. The diameter of the graft was 7.5 – 9.0 mm.Previous studies described in detail the used graft and surgical technique  and the Elevated postoperative IOP was treated as described by Maier et al. , 21.If indicated, re-bubbling (injection of air in the anterior chamber) was performed in the early postoperative period for graft detachments of more than 2 clock hours with corneal edema in more than 2 clock hours.First treatment option for all patients with postoperative IOP elevation was a medical therapy (mainly eye drops) to control the IOP.In steroid-induced glaucoma, normalizing of IOP was achieved by tapering down topical steroids and giving additional glaucoma medication to treat IOP elevation.Post-DMEK glaucoma was treated by additional topical therapy or an additional surgical intervention.p value < 0.05). Possible risk factors included preoperative RNFL, preoperative IOP, pre-existing glaucoma according to the mentioned glaucoma classification, pre-existing glaucoma surgery, preoperative glaucoma medication with carbonic anhydrase inhibitors, preoperative diagnosis as indication for DMEK, post-DMEK glaucoma, postoperative tapering steroid eye drops < 1 year, re-bubbling, or graft rejection. For patients with need for re-bubbling, we checked the risk factors pre-existing glaucoma according to the glaucoma classification, pre-existing glaucoma surgery, preoperative diagnosis as indication for DMEK, type of DMEK surgery  and patients and donor age for an association. The proportional hazard assumption was checked and a p value of < 0.05 was considered statistically significant.Statistical analysis was performed using IBM SPSS statistics 19 . Normality was tested for all outcome measures and the appropriate statistical test was used. Descriptive statistics were expressed as median and range or mean ± standard deviation (SD). We used Kaplan–Meier survival analysis to estimate cumulative incidences. Graft rejection was defined as 1—graft rejection-free survival and graft failure as 1—graft survival. Data of patients after their last presentation in our clinic are censored. In order to identify independent risk factors, we built a COX regression based on bivariable associations with graft rejection or graft failure. All possible risk factors were checked for an association with the outcome and were included in the COX regression if we found evidence for an association  were included in the study. Mean follow-up was 26.1 ± 22.4 months. 75.3% reached a minimum 340 days follow-up and 28.0% a minimum 1050 days follow-up without graft failure.All surgeries were performed by three experienced surgeons , who completed the learning curve of minimum 50 DMEK surgeries beforehand. We summarized the demographic, surgical, and preoperative results in Table p < 0.001, preoperative 1.09 ± 0.59 (n = 150) LogMAR versus 0.46 ± 0.53 LogMAR after 12 months (n = 112) and 0.39 ± 0.44 LogMAR after 36 months (n = 38).Mean visual acuity improved significantly after surgery (2 ± 193/mm2 (n = 147 grafts) to 1739 ± 573/mm2 after 12 months (n = 44) and 1551 ± 393/mm2 after 36 months (n = 14).Mean endothelial cell density decreased from 2268/mmThe 12-month and 36-month cumulative incidences of post-surgical IOP elevation and post-DMEK glaucoma are shown in Table In 12 of the 30 eyes (40%) of steroid-induced glaucoma, the IOP rose again after topical steroids had been tapered down and these eyes developed a post-DMEK glaucoma as defined before , 22.In total, 43 eyes developed a post-DMEK glaucoma, 27 eyes without pre-existing glaucoma surgery or iridotomy/iridectomy, one eye with GDD implant, 12 eyes after preoperative trabeculectomy and 3 eyes after other glaucoma surgery. Topical therapy was sufficient to control the IOP in 19 eyes (44%) with post-DMEK glaucoma. In 24 eyes (56%) with post-DMEK glaucoma, an additional surgical intervention was necessary to control the IOP. Performed surgical interventions included in 10 eyes filtrating surgeries (trabeculectomy or XEN stent implantation), in 7 eyes cyclodestructive interventions (cyclophotocoagulation), in one eye trabectome procedure and in another eye iStent inject® implantation. In 5 eyes, multiple surgical interventions were performed including filtrating surgery, cyclodestructive procedures and interventions at the trabecular meshwork.In 34 eyes (22.7%), a re-bubbling procedure was performed. In 29 of these eyes, one re-bubbling procedure was necessary to achieve a graft re-attachment, in 3 eyes two, in one eye three, and in another eye four re-bubbling procedures. After re-attachment, the corneal edema decreased in 29 eyes during the following 2 to 3 weeks. In 5 other cases, the corneal edema persisted. In two of the cases, intraoperative unfolding and attachment of the flap were not possible due to very flat anterior chamber combined with extensive vis à tergo during the surgery. In these eyes a perforating keratoplasty was performed during the follow-up period. Three other eyes showed no reduction of the corneal edema despite attached flap and received a re-DMEK procedure.p = 0.332, χ2 = 3.411, df 3), pre-existing glaucoma surgery , preoperative diagnosis as indication for DMEK , type of DMEK surgery   and patients (p = 0.982) and donor age (p = 0.079), showed a significant association with re-bubbling.None of the analyzed risk factors, pre-existing glaucoma according to the glaucoma classification  and the pre-existing glaucoma surgery filtrating surgery via GDD implantation compared to no surgery/iridectomy  in the COX regression analysis. Preoperative glaucoma medication, glaucoma diagnosis, re-bubbling, and post-DMEK glaucoma did not influence the risk for graft failure (p > 0.05).The 12-month cumulative incidence of graft failure was 5.5% [CI 95% 1.4–9.6], the 36-month cumulative incidence 16.6% [CI 95% 8.4–24.8]. Kaplan–Meier curve of graft failure is shown in Fig. After DMEK surgery, in eyes with pre-existing glaucoma, IOP elevation occurs in more than 50 percent of patients and a third of patients develops a post-DMEK glaucoma. In these eyes, 3-year incidence of graft rejection is approx. 9%, but not increased by any analyzed risk factor. Graft failure occurs with a 3-year incidence of approximately 17%. A pre-existing filtrating surgery via GDD implantation, but not trabeculectomy, and the indication for DMEK “status after graft failure” increased the risk of graft failure. However, the re-bubbling rate was not increased and influenced by any risk factor, not even by the kind of pre-existing glaucoma surgery.Twelve-month incidence of post-surgical IOP elevation ranges from 6 to 16% and of post-DMEK glaucoma from 2 to 4% in patients after DMEK surgery , 21–27. In this study, steroid-induced IOP elevation was a common cause for postoperative IOP elevation, but proportionally less often than in other DMEK patients , 22, 28.Topical therapy was sufficient to control the IOP in only 44% of eyes with post-DMEK glaucoma, whereas 56% needed an additional surgical intervention. The need of a surgical treatment of post-DMEK glaucoma in eyes with pre-existing glaucoma as described previously , 22 is pGraft rejection rate after DMEK is increased for patients with pre-existing glaucoma with a 3-year incidence of approx. 9% compared to published data with a graft rejection rate between 2.3 and 2.6% after 4–7 years , 30. DifReasons for the high graft rejection rate may be that steroids have to be reduced earlier due to a high incidence of steroid-induced IOP elevation as shown by our data. Additionally, postoperative IOP elevation itself, which occurred very often in patients with pre-existing glaucoma, may trigger graft rejection as shown for perforating keratoplasty. Analyzing our data, we found no independent risk factor for graft rejection. Neither the earlier tapering down of the postoperative steroids nor a postoperative IOP elevation nor pre-existing factors increased the risk of a graft rejection. Both Boutin et al. and Sorkin et al. discussed a more aggressive postoperative steroid regime because of a high rejection rate and a low steroid response rate in eyes that had previously undergone trabeculectomy and/or GDD implantation , 14 whiln = 10).For perforating keratoplasty and DS(A)EK as well as DMEK, graft failure rate in patients with pre-existing glaucoma is increased , 19, 31.The preoperative indication for DMEK “status after graft failure” was the second significant independent risk factor for graft failure. As shown by other studies, these patients have a higher risk for an additional graft failure .The preoperative glaucoma medication with carbonic anhydrase inhibitors, glaucoma classification, preoperative IOP, post-DMEK glaucoma, or re-bubbling did not influence the risk for graft failure. The impact of re-bubbling on the endothelial cell density and graft failure rate had been analyzed before and different studies showed an increased endothelial cell loss after re-bubbling but no reduced graft survival , 35–37 aThe retrospective nature of the study and the wide range of follow-up duration limit the results. Especially, the endothelial cell density can only be assessed to a limited extent because of the fact that a reliable endothelial cell count is not possible in most cases of failed grafts and because of missing data due to missing follow-up examination. As common for a “real world” clinical setting, the cohort is heterogenous concerning pre-operative glaucoma diagnosis, pre- and postoperative glaucoma treatment, and DMEK indication. We took this inhomogeneity into account by using the COX regression analysis. Additionally, there are limiting factors regarding IOP elevation and its measurement, especially in secondary glaucoma after DMEK, which have to be taken into account as discussed in detail before by our group , 19–22.Pre-existing glaucoma increases the risk of graft rejection and graft survival after DMEK. In particular, a pre-existing filtrating surgery via GDD implantation but not trabeculectomy and a DMEK after graft failure amplify this effect on the risk of graft failure. In conclusion, DMEK surgery can improve vision in most cases of eyes with pre-existing glaucoma, but is associated with a higher complication rate and glaucoma monitoring is mandatory.