Orchis mascula L. spanning a large-scale climatic gradient in northern Iran were used to investigate patterns of genetic diversity and plant functional traits. Populations were sampled from three land cover types . Plant height, stem length, number of flowers, bulb fresh and dry weight, glucomannan, and starch concentrations showed high variation among populations and were significantly related to land cover type. In general, genetic diversity was high, particularly in those from eastern Hyrcanian; additionally, populations showed a high level of genetic differentiation (G'st = 0.35) with low gene flow (Nm = 0.46). The majority of genetic differentiation occurred within populations (49%) and land cover types (20%). The population structural analysis using the AFLP marker data in K = 4 showed a high geographical affinity for 198 O. mascula genotypes, with some genotypes having mixed ancestry. Temperature and precipitation were found to shape genetic and phenotypic variation profoundly. Significant isolation by the environment was observed, confirming the strong effect of environmental variables on phenotypic and genetic variation. Marker-trait association studies based on MLM1 and MLM2 models revealed significant associations of P-TGG + M-CTT-33 and E-AGG + M-CGT-22 markers with plant height and glucomannan content. Overall, a combination of large-scale climatic variables and land cover types significantly shaped genetic diversity and functional trait variation in O. mascula populations.Understanding how environmental factors shape patterns of genetic and phenotypic variations in a species is necessary for conservation and plant breeding. However, these factors have not yet been completely understood in tuberous orchid species used to make ‘Salep’, an important ingredient in traditional medicine and beverages in middle eastern countries and India. In many areas, increasing demand has pushed species to the brink of extinction. In this study, 198 genotypes from 18 populations of the endangered species  They often display plasticity and genetic variation along environmental gradients, as these gradients can affect gene flow among populations or create extreme abiotic conditions. Interactions between the environment, phenotypic and genetic diversity have been studied in different plant species at the population level, emphasizing the effect of climate gradients 3. Genetic diversity is crucial since it can act as a buffer against abiotic or biotic stress, anthropological disturbance, inbreeding depression, and interaction with invasive species4. Populations with a history of overexploitation and fragmentation can face genetic depletion that can severely impact the survival and evolution of the species by negatively affecting fitness characteristics7. Under extreme conditions such as prolonged drought or high temperature, high genetic diversity is a significant advantage given the higher possibility of having genotypes/alleles that vary in their response to extremes8, enabling adaptation to environmental changes9. A species' capability to occupy new niches can also be affected by the loss of genetic diversity as well as damaging population growth and breeding12. A population's response to the environment  can additionally be examined using functional traits, traits that are thought to have a profound influence on plant fitness15. The plasticity of these traits under various environmental conditions is a fundamental mechanism that plants rely on to react to environmental changes. The convoluted relationship between genetic diversity, functional traits, and environmental heterogeneity of the locations at the population level has been the subject of quite a few studies17 and deserves more attention.Environmental factors play a key role in shaping patterns of genetic diversity and phenotypic variation in plant species18, which traditionally has been an essential ingredient of confectioneries19. In addition to conferring aromatic and flavor to formulations, it also is a thickening and stabilizing chemical agent20. Salep is integral to several medicines, beverages, and ice-creams21. Its critical rheological properties are due to the presence of polysaccharides, especially glucomannan 18, that is, natural fibers with high water solubility. These also have specific medicinal benefits such as normalizing blood sugar, inhibiting liver irregularities, and alleviating pancreas stress22. In recent years the harvest of tuberous orchids has increased due to high prices fueled by international demand, mainly from Iran's neighboring countries such as India, Turkey, and Pakistan23. Increasing public awareness of salep's benefits and growing demand is a dangerous combination that may jeopardize the long-term viability of natural populations of these orchids.Tuberous orchids have a rich history of traditional use and have suffered from overcollection. The dried tubers of specific wild terrestrial orchids are milled to make a flour known as 'salep'25. Agricultural expansion, urbanization, and silviculture have exacerbated the loss of genetic diversity in orchid populations27. Such problematic circumstances have already taken their toll on orchids in Iran, and some orchid species, including Orchis mascula L., have been classified as endangered26. Therefore, conservation strategies are needed to maintain the genetic diversity in the remaining populations and assure the species' long-term viability29. However, the absence of reliable information on the orchid's population status or the ecological consequences of severe over-collection prevents the implementation of robust conservation plans.Besides the ongoing over-exploitation of the wild population of tuberous orchids, climate change may also hasten the extinction of orchid species27. Recently, numerous small-scale regional studies on the population genetic status of tuberous orchids in the west of Iran have been reported32. However, the data required to develop conservation programs to protect genetic diversity or to develop the germplasm stocks in breeding programs can only be gained through sufficiently large-scale analyses, preferably at the landscape level34. O. mascula is a relatively small perennial orchid widely distributed across Europe, western Asia, and northern Africa. In Iran, this species is distributed primarily in the north (Alborz Mountains) and the west (the Zagros Mountains)36. It is one of several widely collected terrestrial orchids with numerous traditional medicinal applications. Overcollection of tubers has reduced O. mascula population sizes to the point that this species is now endangered; thus, fundamental large-scale studies of the functional traits and genetic diversity responses are needed. This study aimed to sample a high number of individuals from 18 populations of the terrestrial orchid O. mascula in northern Iran to address the following questions: (1) does phenotypic plasticity in functional traits exist in response to environmental heterogeneity? (2) how is genetic diversity and population structure affected by the land cover pattern and climatic variables? (3) do any population(s) have superior biochemical and phenotypic traits to be utilized in breeding programs?Although in Iran, as in other countries, legislators have enacted detailed regulations in favor of germplasm protection. Unfortunately, these regulations have not been enforced properly, and current measures are not limiting the collection or marketing of natural resources, particularly orchid tubersp ≤ 0.05 and p ≤ 0.01); the same applies to the population effect , Ziarat (ZIR), Azarshahr (AZS), and Maraveh Tapeh (MVT) populations overall had the highest values in general with the last population MVT, holds the most significant values. When it comes to floral traits, the variation across the populations is almost intangible Table . The MVTThe HCPC provides information on the factor map resulting from principal component analysis based on phenotypic and biochemical traits of the populations with a three-dimensional hierarchical tree, thereby providing a clearer view of the clustering pattern Fig. . DimensiTwo-way cluster analysis (heatmap) identified two main groups for phenotypic and biochemical traits Fig. , column.P < 0.01), the same applies to GLN  and weakly with longitude. BWF and BDW weakly and negatively correlated to land cover (r =  − 0.4), but there was no correlation between land cover and other phenotypic traits  and weakly with annual precipitation (r = 0.5). Also, it was negatively correlated with longitude (r =  − 0.4) and showed no correlation with annual mean temperature. However, the GLN content significantly decreased with the increase in geographical altitude, annual precipitation, and land cover , respectively. A seesaw correlation was unveiled between GLN and starch , as observed earlier in Table Correlation analyses revealed that the phenotypic traits negatively correlated with the annual precipitation and geographical region altitude and positively correlated with longitude. The important traits, PLH, number of flower (NF) and NLP responded positively to annual mean temperature , while AZS had the lowest value (1.65) among populations with an average of 1.89. The highest effective alleles (Ne = 1.64) were related to the TAS population, and the lowest Ne (1.21) belonged to the ZIR population with an average of 1.46 (Table P%) was 31.14%, while MVT from Golestan had the highest (65.63%), and PAJ was the lowest (18.75%). With an average of 0.35, the highest value of Shannon's information index (I) was observed in the JAD population (0.49), while MSL and PBD populations (0.25) had the lowest level. An average of 0.42 expected heterozygosity (He) and 0.30 for Nei's gene diversity index (H) were observed. The parameters of He and H were the highest in the KAH population , and the MSL population  showed the lowest. The average of the populations' total genetic diversity (Ht) was 0.37, and the genetic diversity within populations (Hs) was 0.24.At the population level, TAS, MSL, and CHP had the highest observed alleles (46 Table . Among tG'st) was 0.35, indicating that 35% of the genetic variation was among the populations, and 65% was within the populations. The G'st was higher than average, considering that G'st between 0.05 and 0.15 is defined as moderate and values over 0.30 as high37. Thus, G'st reflected the significant genetic differentiation among the populations. These findings were consistent with the relatively low gene flow value (Nm = 0.46) observed among populations  with a significant share of total genetic diversity (48%), whereas a larger proportion of variation (52%) existed within populations .The coefficient of genetic differentiation among populations  . The population Nei’s genetic diversity demonstrated a negative correlation to annual precipitation , and positively to longitude . This parameter also potently and negatively correlated to land cover , precipitation in June (r =  − 0.4) and geographical altitude (r =  − 0.5). The population expected heterozygosity also significantly correlated to annual precipitation , negatively correlated to geographical altitude (r =  − 0.5), precipitation in June (r =  − 0.3), land cover (r =  − 0.4), positively with longitude (r = 0.5), annual mean temperature (r = 0.4), mean temperature in May (r = 0.4) and mean temperature in June (r = 0.4)  Fig. , which s.4) Fig. .Figure 8In testing for isolation by distance (IBD), the relationship between geographical and genetic distance and geographic and phenotypic distance were insignificant Table . These rr2 > 0.40 at P < 0.01, where three of the markers revealed a simultaneous association with several important traits. For example, marker P-CCA + M-AGA-49 showed significant simultaneous associations with quantitative traits PLH, STL, NLP, LL, IL, NF, LLI, LMLL, BFW, and BDW on both MLM1 and MLM2 models  as opposed to the woodlands of Mazandaran and low altitudes of Guilan. The determinative effect of light was studied by41, who found that the overall growth rate was higher in coppiced than undisturbed woodland. Although plant species respond differently to shade, many usually do not succeed in flowering under deep shade42. In addition to light, higher nutrient availability in clear-cuts and pastures43 may have facilitated better growth performance.In this study, we observed a strong pattern of phenotypic response to environmental variables, revealing the importance of land cover type and precipitation. Orchis purpurea Huds.44, and in O. mascula and O. pauciflora Ten.45. The results of the current study were, to a large extent, consistent with these studies. In the comprehensive phenotypic study of Ebrahimi, et al.26. quantitative floral traits showed considerably higher variation than the results reported here; however, the number of flowers had the highest coefficient of variation (41.34%), similar to our results  showed an inverse relationship 22 were notably lower than our observation  at the population level with an average of 0.30 was observed to be higher than the previous studies on O. mascula; for example, Jacquemyn, et al.41 reported an average of 0.16 or Gholami, et al.31 0.10. However, the latter used ISSR markers with a small sample size (5). Strong geographical affinity seemed to be followed by the pattern of genetic diversity among populations, analogous to phenotypic variation, the level of genetic diversity was notably higher in the populations of Golestan, where KLH had the highest H (0.39) and other populations from this province similarly showed high H . Thus, the possibility of pollinators visiting more flowers available before leaving due to the food-deceiving nature of this species is higher; however, the gene flow (Nm) mean was low.In this study, we found that genetic variation among G'st: 0.35, Table 56. Low within-population genetic diversity (52%) and high differentiation (G'st: 0.35) in this study are contrary to previous results reported on O. mascula, showing a high genetic diversity within population (92%) and low G'st (0.125)41. Moreover, the woodland type did not affect genetic partitioning. This is in sharp contrast with the results of this study, where 20% percent of the total genetic diversity was partitioned among three groups of land covers . Differences in scale can explain these contrasting results. Whereas Jacquemyn, et al.57 studied 15 populations in the local landscape with slight variation in climatic conditions, in our study, populations were studied across a much larger scale leading to strong differences in climatic conditions between eastern and western populations and significant IBE. This suggests that populations have genetically adapted to the different precipitation regimes across Hyrcania and that this genetic adaptation has contributed to the observed high genetic differentiation. These results further support the findings of Siepielski, et al.58, who showed that precipitation was a dominant driver of natural selection in plant and animal populations for over 150 species.The genetic structuring of populations was strong in K = 2 and K = 4 Figs.  and 7a, O. mascula population sizes have been reduced beyond recovery in many areas in the west, northwest, and north of Iran. Developing a breeding program could be a sustainable option to counteract the further loss of populations. Selection of the most suitable populations and cultivation of local plant material could represent the first steps in a sustainable management of O. mascula populations in the wild. Our results showed that populations from Golestan possessed several important desirable traits, including a high GLN content and bulb dry weight that meets the high-quality threshold48. Another approach to probe for favorable traits is the percent of variation (%var) populations display59. These traits mentioned above also had the highest variation percentage under the effects of land cover and population  in which access to any wild population requires official permission. Dr. Gholizadeh identified the species identity of the samples, and voucher specimens from the populations were deposited in the University of Guilan Herbarium , available for botanical studies with an official request.In the spring of 2020, eighteen populations of on Table , followiA total of 198 individuals from 18 populations, with at least 20 m distance between individuals in each population, were collected from these populations. Nineteen quantitative phenotypic traits related to the size of the plants and flowers Table  were rechttps://www.esri.com/en-us/arcgis/products/arcgis-desktop/resources), a program used to calculate the areas and percentages of land use and land cover by each category.Young and fresh leaves were collected from each individual, immediately dried with silica gel, and transported to the laboratory for DNA extraction. For each population, the land cover was characterized by assessing the percentage of vegetation . Using Google Earth Pro®, high-resolution aerial images of the locations were acquired. The interpretation of the images was based on visual classification using the ArcGIS 10.4® software . The dried specimens were first cut into small pieces and then powdered into flour by milling. Further analysis was carried out to measure the components of interest in the flour.To produce salep powder from bulbs, a traditional method previously described by Tekinşen and Güner1, A2, A3; for starch ΔA, F) in a UV–Vis Spectrophotometer (Shimadzu – UV Mini 1240) at 340 nm (for GLN) and 510 nm (for starch) and using the following formula 1 and 2, respectively65:The samples were prepared according to procedures specified for GLN and starch using GLN (K-GLUM 10/04) and total starch  assay kits . The GLN and starch contents of the samples were determined by measurement of absorbance values of prepared blind and sample solutions , the pigment was first extracted with 0.5 mL methanol/0.1% HCl for 24 h in the dark. Then the absorbance was read at 510 nm using a UV–Vis spectrophotometer 67. The quality and quantity of extracted DNA were assayed by 1% agarose gel electrophoresis and NanoDrop® spectrophotometer and diluted with sterile distilled water to give a final 100 ng/μL. The method described by Vos, et al.68 was employed with slight modification for AFLP analysis. An amount of 250 ng genomic DNA (per sample) was homogenized with EcoRI and MseI primers without a selective base in pre-amplification and with three selective bases in amplification in 20 µL master mix . The obtained combination of genomic DNA and restriction enzymes were incubated for 12 h at 30 °C. Then double-stranded EcoR I (Eco) and Mse I (Mse) linkers were ligated to the restriction fragments without additional nucleotides and afterward amplified by primers with three additional nucleotides at the 3′-end (Table 69. The banding pattern of the amplified DNA fragments was turned into binary codes as presence (1) or absence (0), and only the consistently repeatable bands were scored. Weak or smeared bands were excluded, and the fragments of the same molecular weight were counted as the same locus.Genomic DNA was extracted from silica gel dried young leaves of the individuals utilizing CTAB procedureA principal component analysis (PCA) was performed on the phenotypic data. The first two principal components were retained as they explained the highest portion of the total variation and were treated like traits and subjected to further analysis. To determine how phenotypic variation is partitioned between different types of land covers (fixed effect), among populations (random effect), and among individuals (error), an unbalanced restricted maximum likelihood (REML) mixed-model analysis of variance (ANOVA) was conducted for the 22 measured traits and PC1 and PC2  using "Factoextra" and "FactoMineR" packages73. The same package was utilized to assess the relationship between genetic diversity indices and environmental factors .A correlation analysis between phenotypic traits was carried out using Pearson's correlation coefficients (P 0.01) using the "CorrPlot" packageNa), the effective number of alleles (Ne), expected heterozygosity (He), Shannon's diversity index (I), Nei's genetic diversity (H), total heterozygosity (Ht), and intra-population genetic diversity (Hs) were computed using POPGENE v3.074  was carried out using the "logisticPCA" package77 in the R software to visualize the spatial structure. Bayesian model-based cluster analysis using STRUCTURE v2.3.478 was also used to infer genetic structure and define the number of clusters in the data set. The correlated allele frequencies and admixed model were applied with 50,000 burn-in and 100,000 MCMC. The assumed number of groups (K) varied from 1 to 10, and 10 runs per K were performed. The STRUCTURE HARVESTER79 determined optimum K based on L(K) and ΔK. The structure result at K = 2 and K = 4 were mapped according to geographical locations using the ArcGIS 10.4® software (URL: https://www.esri.com/en-us/arcgis/products/arcgis-desktop/resources).A Cluster (heatmap) analysis was performed at the population level based on the matrix of Nei's unbiased genetic distance using "ggplot2"80 in the R software. Shannon differentiation coefficient (G'st) was calculated based on the following formula, G'st = (Hsp − Hpop)/Hsp , and the gene flow (Nm) among populations was estimated using POPGENE v3.074.To assess the magnitude of genetic differentiation, the partitioning of total genetic variation at different scales was computed with analysis of molecular variance (AMOVA) in "vegan"81 and phenotypic distance using the "dist" function in R software. Euclidean geographic distances were calculated using the "fossil" package82. For sampling sites, climatic variables were collected from WorldClim version 2.067. Bioclimatic variables (19) were considered as different environmental space vectors and used the Canberra distance to calculate the distance between populations in this vector space. For isolation by distance (IBD) and isolation by environment (IBE), the relationship of genetic and phenotypic distances with geographic and environmental conditions was analyzed, respectively, using simple and partial Mantel tests implemented in the R package "vegan"80. Additionally, all other possible comparisons  were tested.To test the effects of geographic distance and environmental differences on the genetic and phenotypic structure, the genetic distances among populations were calculated using Nei's genetic distance implemented in R package "Poppr"http://www.maizegenetics.net/bioinformat-ics) 83: a Mixed Linear Model (MLM) using the kinship matrix (K) estimated by the STRUCTURE HARVESTER79 as a random effect (MLM1) and an MLM using both Q and K (MLM2). Results were compared to determine a better model. Significance of associations between loci and traits was described as P-values (a probability level of 0.001).Two models were compared to test the marker-trait association between AFLP and phenotypic and chemotypic traits using TASSEL 4.0.1. (Supplementary Information.


Human gut microbiome has gained great attention for its proposed roles in the development of hypertension. The fungal microbiome in the human gut (i.e. the mycobiome) is beginning to gain recognition as a fundamental part of our microbiome. However, the existing knowledge of human mycobiome has never revealed the association between gut mycobiome and hypertension. It is known that inflammation and immunity contribute to human hypertension. Here, we sought to investigate whether gut mycobiome could predict the development of hypertension and its association with immunoglobulin light chains.Participants were classified into three cohorts: prehypertension (pre-HTN), hypertension (HTN), and normal-tension (NT) based on their blood pressure. Fresh samples were collected, and the ITS transcribed spacer ribosomal RNA gene sequence was performed. An immunoturbidimetric test was used to examine the serum levels of immunological light chains.Malassezia increased in the HTN group compared to that in the NT group, and the relative abundance of Mortierella enriched in the NT group. For the pre-HTN group, the relative abundance of Malassezia was positively associated with serum the concentration of light chain (LC) κ ; for the HTN group, the relative abundance of Mortierella was positively associated with the serum concentration of LC κ (P<0.05), the relative abundance of Malassezia was positively associated with both the serum concentrations of LC κ and LC λ .Subjects in both of the states of pre-HTN and HTN had different fungal microbiome community compared to the NT group (FDR<0.05). Slightly higher levels of fungal richness and diversity were observed in the groups of pre-HTN and HTN. The relative abundance of Our present study demonstrated that gut fungal dysbiosis occurred in the state of prehypertension, and fungal dysbiosis can predict the dysregulation of serum light chains in hypertension patients. Further study on modulating gut fungal community should be focused on balancing the immunological features in hypertension. Estimates suggest that 31.1% of adults worldwide had hypertension . There iMost studies describing the human gut microbiome in health states have been focused on the bacterial component, but the fungal microbiome  is beginning to gain recognition as a fundamental part of our microbiome . DysbiosExperimental studies implicate inflammation and immunity contribute to human hypertension . The infIt is demonstrated that the activation of the immune system either directly or indirectly impacts the gut microbiome . HoweverIt is known that T2DM and fatty liver disease are related to impaired metabolic homeostasis and can be regarded as a metabolic disorder , 15. LikParticipants were classified into three groups according to their blood pressure (BP), namely, pre-HTN (n=38), HTN n=46), and NT (n=34). The presence of HTN was confirmed as a systolic blood pressure (SBP) ≥140 mmHg or a diastolic blood pressure (DBP) ≥90 mmHg and/or use of antihypertensive medication; pre-HTN was defined as an SBP of 120–139 mmHg or a DBP of 80–89 mmHg without the use of antihypertensive medication; Normal-tension BP (NT) was defined as an SBP ≤ 120 mmHg or a DBP ≤80 mmHg . Accordi, and NT The participants were instructed to collect their fecal samples to a sterile container and pick approximately 30mg of feces to a sterile bottle which contained 1000μL lysis buffer composed of Tris 0.1mol/L (pH 8.0), 2 mM EDTA, and 2%SDS , and stored at −80°C until further processing.The blood samples for detecting immunological LC were collected on the day of fecal samples collection. An immunoturbidimetric test was used to assess serum levels of immunological features on the day of sample collection . Information on clinical manifestations, concurrent diseases, and blood parameters were assessed by reviewing clinical records, medical interviews and face to face interview. In addition, the nutrient intake was assessed using a Chinese Food Frequency Questionnaire .DNeasy PowerSoil Pro Kit was used to isolate fungal genomic DNA from fecal samples according to the manufacturer’s instructions in a biological safety cabinet , with additional glass-bead beating steps performed using a Mini-Beadbeater . The amount of DNA was determined using a NanoDrop ND-1000 spectrophotometer (Thermo Electron). The integrity and size of DNA were verified by electrophoresis on a 1.0% agarose gel containing 0.5 mg/ml ethidium bromide. All DNA samples were stored at −20°C prior to further analysis.°C for 15 sec for 1 cycle, then 98°C for 15 sec, 58°C for 15 sec, then 72°C for 15 sec for 30 cycles, followed by a final extension at 72°C for 60 sec for 1 min. Negative DNA extraction samples (lysis buffer and kit reagents only) were amplified and sequenced as contamination controls. Amplified products were purified using Agencourt AMPure XP beads  and samples were run on a 1% agarose gel in order to size-select gel slices around 430 bp. The amount of DNA was determined using a Qubit 2.0 Fluorometer . Sequencing was performed with 2×150bp on a Novaseq 6000 platform .The ITS regions were amplified using ITS1F (5’-CTTGGTCATTTAGAGGAAGTAA-3’) and ITS2  primers (https://unite.ut.ee) database (The ITS sequence dataset was merged and demultiplexed into per-sample data using QIIME (V1.9.1) with default parameters . Raw seqdatabase .An OTU table was further generated to record the abundance of each OTU in each sample and the taxonomy of the OTUs. A minimum library size was chosen to rarefy the OTUs in our present study, as it is critical to normalize the OTU table to eliminate any bias due to differences in the sampling sequencing depth. And total sum scaling (TSS) was applied to transform the OTU table into relative abundance by dividing the number of total reads of each sample. OTUs containing less than 0.01% of total sequences across all samples were discarded.via principal coordinate analysis (PCoA) based on permutational multivariate analysis of variance [PERMANOVA] calculated by ‘adonis’ function.Sequence data analysis was performed using QIIME and R package (V3.2.0). OTU-level alpha richness and diversity indices, including Chao 1, Shannon and Simpson, were calculated using the OTU table. Beta diversity analysis was performed to investigate the structural variation of fungal communities across samples using Bray-Curtis metrics and visualized t test and ANOVA were used on normalized continuous variables and Wilcoxon rank-sum test on non-normal continuous variables. The P-value was adjusted for multiple comparisons using the Benjamini–Hochberg (BH) false discovery rate (FDR). Pearson correlation analysis was performed on the abundant bacterial genera (>1% relative abundances) and LC κ, LC λ and κ/λ ratio that differed between groups.Pearson’s Chi-square or Fisher’s exact tests were used with categorical variables; Student’s We collected fecal samples from 38 subjects with pre-HTN, 46 with HTN and 34 gender-age-BMI matched NT Table 1.2 = 0.032, FDR=0.036 and R2 = 0.025, FDR<0.037, respectively; 2 = 0.023, FDR=0.414; Wilcoxon rank-sum test, FDR>0.05; When comparing to the NT group, we found that the fecal mycobiome of both pre-HTN and HNT groups differed , Malassezia (17.78%), Aspergillus (14.46%), Cladosporium (12.79%), and a member of the bacterial family of Malasseziaceae (11.20%). The dominated bacteria genus in the HTN group were somewhat different from those in the pre-HTN group, such as Malassezia was accounted for 28.45%, followed by Candida (18.26%), Cladosporium (14.30%), Aspergillus (12.13%), and Saccharomyces (8.46%). The NT group was dominated by Candida (21.80%), Aspergillus (19.88%), Cladosporium (19.77%), Wallemia (12.71%), and Malassezia (9.31%).When the bacterial phyla were assessed Parapsilosis and Tropicalis from Candia were predominated in the samples of NT group .The classified species from the dominated fungal genera in the pre-HTN, HTN and NT groups were examined Table S2Mortierellomycota significantly increased in the NT group compared to the pre-HTN group . For example, the HTN group had higher abundance of a member of the family of Debaryomycetaceae than that in the groups of pre-HTN and NT. Hanseniaspora increased in pre-HTN group compared to HTN group. Although Malassezia increased with BP, the difference was only observed between HTN and NT groups. Among the three groups, the NT group had the highest abundances of Mortierella and a member of the bacteria order of Hypocreales.Next, we compared the relative abundances of bacterial phylum and the bacterial genus with above 1% of the total abundance among the three groups of pre-HTN, HTN and NT. We found that It is known that immune cells play a role in hypertension and a previous study evidenced that there was a relationship between white blood cells and blood pressure in hypertension population –25. ThusBMI, age and eGFR are factors influencing blood pressure in hypertension patients –28, thusMalassezia was positively associated with serum concentration of LC κ in the pre-HTN group  can be found in the article/The studies involving human participants were reviewed and approved by The Affiliated Xinchang Hospital approved this study. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.Conceptualization: YZ, YY. methodology: AG, BL, CH, QW, YY. software: CH, QW. validation: YY. writing: YY. supervision: YY. funding acquisition: YY. project administration: CH, QW, YY. All authors contributed to the article and approved the submitted version.


Hepatocellular carcinoma (HCC) is the most common neoplasm and the major cause of cancer-associated death worldwide. The high mortality rate of HCC is mainly attributed to its widespread prevalence and the lack of effective treatment. Immunotherapy as a promising, innovative approach has revolutionised the treatment of solid tumours. However, owing to the heterogeneity and complex tumour microenvironment of HCC, an efficient biomarker for immunotherapy has yet to be identified. We investigated the role of immune-related long non-coding RNAs (lncRNAs) as prognostic biomarkers in patients with HCC from The Cancer Genome Atlas (TCGA) database. Spearman correlation, univariate and multivariate Cox, and lasso regression analyses were utilised to screen lncRNAs associated with prognosis. Four lncRNAs were filtered out to develop an immune-associated lncRNA prognostic signature in TCGA training as well as validation cohorts. Patients with HCC were then categorised into low- and high-risk groups according to the median value of the risk scores to evaluate the ability of the prognostic model between training and validation cohorts. A nomogram (based on risk score and stage) was constructed to appraise the general overall survival (OS) of patients with HCC. Differences in immune cell infiltration, immune checkpoint inhibitor (ICI) treatment response, gene mutation, and drug sensitivity were observed between the two groups. Thus, the lncRNA prognostic signature can serve as a sensitive prognostic biomarker with potential in individualised immunotherapy for HCC patients.The online version contains supplementary material available at 10.1186/s12876-022-02540-2. The clinical data of patients with HCC were then combined with the IRL expression matrix . The remaining people were divided into a training set and a verification set. Univariate, multivariate Cox, and lasso regression analyses were employed to select IRLs linked to prognosis. Four lncRNAs were recognised to build an IRL prognostic signature. The AUC values of the training and verification sets were 0.835 and 0.822, respectively, indicating an outstanding ability of the lncRNA signature to assess prognosis. We then investigated the correlation between risk scores and clinical factors and plotted a nomogram and calibration curve. Differences in immune cell infiltration, immune checkpoint inhibitor (ICI) treatment response, gene mutation, and drug sensitivity were observed between the high- and low- risk groups. Therefore, this lncRNA prognostic signature can be used as a sensitive biomarker to predict the prognosis of patients with HCC and can positively impact personalised immunotherapy.Hepatocellular carcinoma (HCC) is the sixth most common malignancy worldwide and the fourth leading cause of cancer-related death . Even thp < 0.001). Finally, 818 IRLs were screened. The patients’ corresponding clinicopathological characteristics were then combined with the IRLs expression matrix. The 342 patients with HCC were randomly splited into training or verification sets in a 2:1 ratio for building and certifying the IRLs signature  in a univariate Cox proportional exposure regression analysis . The independence of risk signature in HCC was then assessed through univariate along with multivariate Cox regression evaluation. The risk score based on IRLs was significantly correlated with OS  Fig. A. The KEp < 0.05). Tumor immune dysfunction and exclusion (TIDE) was developed based on the two main mechanisms of tumor immune escape by Jiang et al. [, TIDE, Dysfunction and MSI were highly expressed in the low risk group, while Exclusion was the opposite (p < 0.05). Furthermore, we analysed the relationship between possible the immune checkpoints and risk signature. Figure CTNNB1, TTN, and AXIN1 accounted for 27, 25, and 9% of all mutations, respectively, which are slightly higher values than those observed in the high-risk group. Finally, the relationship between risk score and clinical drug sensitivity was analysed. As shown in Fig. We applied seven common suitable methods to appraise the immune cell infiltration, including MCPCOUNTER, XCELL, TIMER, QUANTISEQ, CIBERSORT-ABS, EPIC, and CIBERSORT Fig. A. Positig et al.  which caP < 0.05). We then verified the expression level of lncRNA in normal liver cell (LO2) and liver cancer cells . As shown in Figs. P < 0.05).We explored the lncRNA expression in the high- and low-risk groups. As shown in Figs. TP53 accounted for 42%, a remarkably higher value than that in the group with low risk, consistent with Calderaro’s research, wherein TP53-mutated HCC cells were poorly differentiated, densely packed, highly proliferative, multinucleated, and pleomorphic and exhibited frequent vascular infiltration [CTNNB1 was the most mutated gene, with slightly higher expression than that in the high-risk group. CTNNB1 mutations characterise a particular cholestatic well-differentiated subtype of HCC [p < 0.001) but a lower IC50 for chemotherapy drugs such as doxorubicin (p < 0.001) and gemcitabine (p < 0.001), signifying that our model could be used to predict chemotherapy and targeted therapy sensitivity. However, it is undeniable that there are some limitations to this study. The model was established and validated only using TCGA data, with no external validation from the Gene Expression Omnibus (GEO) or other databases; thus, the final model may be inaccurate. However, various methods were used to verify this novel prognostic risk signature, revealing its superior potential. As a result, we believe that our model is admissible. In the future, we will collect tissue samples, increase the sample size, and perform multi-centre validation to enhance the predictability of the model; nevertheless, these further studies will be expensive and time-consuming. In conclusion, our research developed a new predictive signature derived from IRLs and assessed the role of tumor microenvironment in patients with HCC. The signature may help identify individuals with HCC who could benefit from anticancer immunotherapy and offer possible targets for accurate prediction.HCC is a highly common malevolent tumour with a poor prognosis. There have been tremendous advances in the treatment of HCC over several decades , 37. Nevltration . In the e of HCC , 51. Finhttps://portal.gdc.cancer.gov/). Data of the immune genes were downloaded from the ImmPort database (https://www.immport.org/home). The KEGG pathway enrichment analysis was accomplished from the KEGG pathway database [https://www.kegg.jp/kegg/kegg1.html). In addition, the somatic mutation records of patients with HCC were acquired with a mutation annotation system (MAF) file from TCGA. The data of immune cell content in TCGA HCC patients were acquired in TIMER2.0 (http://timer.cistrome.org).RNAseq information of carcinoma and adjacent tissues in patients with HCC and relevant clinical data were achieved from TCGA website . The somatic mutation database of patients with HCC was obtained from TCGA using an MAF file, and the mutation data were visualised using the ‘maftools’ platform in R software. Using the Caret R package, 342 patients were randomised to either the training or verification cohorts in a 2:1 ratio. The IRLs were then extracted using Spearman’s correlation analysis, and 818 IRLs were filtered out. We used univariate and multivariate Cox regression assessment and LASSO regression analysis to identify the greatest-fit OS-related lncRNAs. Finally, four lncRNAs were found to be the optimised predictive risk signatures, and the scoring system for patients with HCC was derived using the following formula: Risk score = (1.1 × AC099850.3) + (1.16 × NRAV) + (1.10 × ZFPM2-AS1) + (− 0.79 × AC015908.3). The OS predictive performance of the prognostic risk model was evaluated between the training and validation cohorts by dividing patients into low- and high-risk groups based on median risk scores. We used GSEA to better understand the mechanism underlying the novel lncRNA prognostic signature in patients with HCC through R packages “clusterProfiler”, “enrichplot” and “ggplot2”. The gene sets “c5.go.v7.4.symbols.gmt” and “c2.cp.kegg.v7.4.symbols.gmt” were chosen as the reference gene set. Simultaneously, we also utilised the ssGSEA technique to quantify immune cells and processes involved in the mechanism. IC50 was calculated using the R package pRRophetic, and the IC50 in high- and low-risk groups was evaluated employing the Wilcoxon signed-rank test. Spearman correlation analysis was also used to analyse the relationship between the risk score, immune checkpoint, and drug sensitivity. The ggplot2 R platform  was utilised for visualisation.2.The normal hepatic cell (LO2) and liver tumour cells  were kindly provided by liver surgery laboratory of Tongji. LO2 cells were cultured on 1640 medium , and the other cell lines were cultured on DMEM. Cell culture was performed under standard cell culture conditions in humidified 5% COGAPDH as an internal reference.RT-qPCR was performed on an ABI 7900 qPCR system using ChamQ Universal SYBR qPCR Master Mix reagent (Vazyme), following the manufacturer’s instructions. The primers used for RT-qPCR were synthesised by Tsingke Biological  and the associated packages were used for all computational and statistical studies. Two-tailed Additional file 1: Table 1. Clinical characteristics of hepatocellular carcinoma in train and validation cohort.Additional file 2: Supplementary Table 2. 76 prognostic associated lncRNAs.Additional file 3: Supplementary Figure 1. (A) The best-fit OS-related lncRNAs were chosen by Lasso regression analysis. (B) The Lasso regression was performed with the optimal value of λ. (C-D) Distribution of risk scores, survival status. Supplementary Figure 2. (A-D) PCA among all genes, immune genes, immune LncRNA, and risk immune LncRNA.Additional file 4: Supplementary Table 3. All the primer sequences.


Metabolically healthy obesity (MHO), has been recognized as a transient phenotype with few cardiometabolic diseases; however, little is known regarding the development of hypertension in subjects with an absence of cardiometabolic abnormalities and general obesity evaluated by body mass index (BMI) or abdominal obesity evaluated by waist circumference (WC).2). Logistic regression was used to analyze the relationship between MHO and prehypertension (120 < SBP < 140 mmHg or 80 < DBP < 90 mmHg) and hypertension (SBP ≥ 140 or DBP ≥ 90 mmHg). The age- and sex-specific impacts were further analyzed.A total of 4764 participants were enrolled from the China Health and Nutrition Survey and followed up from 2009 to 2015, whose fasting blood samples were collected in 2009. Obesity was classified as abdominal obesity (WC ≥ 90 cm in men and ≥ 80 cm in women) and general obesity (BMI ≥ 25.0 kg/mp < 0.001] and hypertension  than those metabolically healthy but without abdominal obesity. Similar associations were observed in the subjects with metabolically healthy general obesity (MHGO) phenotype, particularly those aged under 64 years. Men with the MHAO phenotype seemed to have higher risks of prehypertension [2.42 (1.52–3.86) in men vs. 1.76 (1.36–2.29) in women] and hypertension [3.80 (2.38–6.06) in men vs. 2.22 (1.64-3.00) in women] than women, when compared with those metabolically healthy but without abdominal obesity.There were 412 (37.9%) participants with prehypertension and 446 (41.0%) participants with hypertension and metabolically healthy abdominal obesity (MHAO). The participants with the MHAO phenotype had significantly higher risks of prehypertension [odds ratio (OR) = 1.89 (1.51–2.36), The MHO phenotype, regardless of the presence of general or abdominal obesity, showed a worse effect on the development of prehypertension and hypertension, particularly in young adults. Abdominal adiposity with a healthy metabolic state is significantly associated with incident hypertension in both men and women. These findings can guide the establishment of risk-stratified obesity treatments.The online version contains supplementary material available at 10.1186/s13098-022-00917-7. The prevalence of overweight and obesity has increased over the past forty years as a result of increased intake of high-fat diets and physical inactivity , 2, amplHypertension is a prominent risk factor for cardiovascular death . The relIn this study, we aimed to explore the value of the combination of metabolic state  and obesity in predicting the development of prehypertension and hypertension. We also aimed to identify the sex- and age-specific relationships between elevated blood pressure (BP) and MHGO and metabolically healthy abdominal obesity (MHAO). It can be meaningful to better understand of whether and how different obesity treatment strategies may promote individual treatment decisions based on the MHGO and MHAO phenotypes.http://www.cpc.unc.edu/projects/china). The first blood sample collection was conducted on a large scale in 2009. We excluded individuals who aged < 18 years (N = 31); lost to follow up in 2015 (N = 5846); lacked BP data in 2009 and 2015 (N = 712); missed fasting plasma glucose (FPG), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), insulin in blood samples (N = 502); and lacked waist circumference (WC) and body mass index (BMI) (N = 74). The flow chart is shown in Fig. The China Health and Nutrition Survey (CHNS) was initiated in 1989 and involved 11,929 participants living in cities and rural areas in nine provinces, including Liaoning, Heilongjiang, Jiangsu, Shandong, Henan, Hubei, Hunan, Guangxi, and Guizhou. This ongoing observational cohort study is representative of Chinese participants due to its large scale and standard conduction. The CHNS is an ongoing, open, prospective cohort study in China, and ten CHNS rounds have been completed, respectively in 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011 and 2015. Participant enrollment and information collection have been previously described . The sur2 at study entry and calculated as body weight (kg) divided by the square of height (m2) to evaluate the general adiposity state. The subjects were asked to rest for at least 10 min before BP measurement. Using standard mercury sphygmomanometers, the trained staff measured the systolic BP (SBP) and diastolic BP (DBP) three times on the right arms of the seated subject hearing Phase I and V Korotkoff sounds. BP was measured three times on the right upper arm in a seated position after a 5-minute rest, with a 2-minute interval between measurements. The average of three BP values was used in the analyses. The mean arterial pressure (MAP) was calculated by the formula MAP = 1/3 SBP + 2/3 DBP (in mmHg).Clinical demographic information, including sex, age, community type, marital status, education level, smoking habits, alcohol consumption and histories of hypertension, diabetes mellitus and related medical treatment, was obtained by trained staff through standardized self-questionnaires. Physical examinations, including height, weight, WC and hip circumference, were performed repeatedly by trained investigators using calibrated beam scales. WC was measured at a midway point between the lowest rib and the iliac crest by a nonelastic tape. BMI was defined as kg/mhttps://www.cpc.unc.edu/projects/china/data/datasets/biomarker-data). Biochemical markers, including HDL-C, low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), TG, urea, serum uric acid, serum creatinine, total protein, albumin and alanine aminotransferase levels, were measured by an automatic clinical chemistry analyzer . FPG was measured by the GOD-PAP method . Apolipoprotein A (ApoA) and apolipoprotein B (ApoB) were measured by immunoturbidimetric methods , and insulin levels were measured by a radioimmunology assay .Fasting blood samples were collected by trained nurses according to the standard protocol and guidelines, and transferred to a national central laboratory in Beijing   and hypertension  than subjects with metabolically healthy but without abdominal obesity phenotype  in men; 1.76 (1.36–2.29) in women] and hypertension , when compared with those metabolically healthy without abdominal obesity (Table We classified subjects into young subjects (aged ≤ 64 years) and elderly subjects aged ≥ 65 years), as previously reported . As show years, aThe MHO phenotype has been well established, for its absence of metabolic and cardiovascular complications, but it is debated whether MHO, particularly MHAO, should be intervened to prevent incident hypertension. Based on this nationwide population study, we confirmed the association of MHO phenotype with the risks of prehypertension and hypertension, regardless of general or abdominal obesity.A recent study has shown that overweight/obesity has a prevalence of 51.2% and hypertension has a prevalence of 27.5% in China, both estimated to reach 70.5% and 35.4% in 2030 . The preOur findings showed that the prevalence and risk of hypertension were higher in those with general obesity or abdominal obesity, regardless of metabolic state, compared with those metabolically healthy without obesity. The men with MHAO showed higher ORs of hypertension than women with MHAO in our study. It has been reported that adipose tissue tends to accumulate around the abdomen in men, around the hips and thighs in women . Recent The young adults with MHO phenotype aged under 64 years, regardless of general or abdominal adiposity, had significantly higher risks of prehypertension and hypertension than those with MHNO phenotype, but this association did not exist in the elderly with MHNO phenotype. It is well known that aging is another risk factor for CVDs, and the elderly are more likely to have an unhealthy metabolic state. Additionally, the small number of elderly participants with MHO phenotype  in this study may bias our finding, which should be verified in a larger-scale elderly cohort study. Our findings indicated that the MHO phenotype, including general and abdominal obesity, was not an absolutely safe condition, and weight loss could reduce the risk of developing elevated BP, which is consistent with other studies , 37–39. The implications of the MHO phenotype are controversial, but it provides a novel concept to focus on the mechanism underlying obesity-related cardiometabolic complications based on lipid accumulation and weight gain without CAs. Compelling evidence has demonstrated the biological mechanisms and phenotypic characteristics of MHO and MUO phenotypes , 40, 41.This study has several limitations. First, racial homogeneity was a restriction of this study. The CHNS cohort study only represented the Chinese population. Multiple ethnic and large-scale cohort studies are needed to validate our results. The absence of data on fat distribution was another limitation because fat deposition in different parts of the body has a different physiological basis and plays different roles in hypertension. Subjects with hypertension at baseline may exaggerate the predictive value of MUO phenotype for incident hypertension. Additionally, the lack of adjustment for biochemical data at the last visit also challenges the reliability of our findings. Finally, the lack of standardized BP measurements at 1 to 4-week intervals (depending on the BP level) may affect the diagnose of hypertension. Despite these limitations, this study was the first to distinguish abdominal and systemic adiposity combined with metabolically healthy status in the development of prehypertension and hypertension. A further analysis on the effects of sex and age on the relationship between metabolic obesity and elevated BP can make our findings more comprehensive.In conclusion, our study added evidence that MHO may be a phenotype of interest in future analysis of hypertension or CVD, which is gleaned from the epidemiologic context. Our findings indicated that the MHO phenotype, regardless of general or abdominal obesity, increased the risks of prehypertension and hypertension, particularly in young adults. Furthermore, metabolically healthy status as a transient state was shown to far increase the risk of hypertension when it changed to a metabolically unhealthy situation. Particular attention should be given to the impact of metabolic state and how it increases the risk of hypertension over time.Additional file 1: Table S1. Comparison of characteristics between target population in 2009 and 2015.Additional file 2: Table S2. Adjusted odds ratios and 95% confidence intervals of the association of metabolic health and general obesity with prehypertension and hypertension by sex.Additional file 3: Table S3. Adjusted odds ratios and 95% confidence intervals of the association of metabolic health and general obesity with prehypertension and hypertension by different age periods.


The relationship between anaesthetic technique and graft patency after open lower limb revascularization is unclear. The aim of this study was to evaluate the association between 30-day graft patency after elective infrainguinal bypass and anaesthetic technique  compared with general anaesthesia (GA)).9/L. The primary outcome was primary graft patency without reintervention. The relationship between anaesthetic technique and patency was analysed with multivariable logistic regression.Patients who underwent elective infrainguinal bypass in the 2014–2019 National Surgical Quality Improvement Program Vascular Procedure Targeted Lower Extremity Open data set were included. Excluded patients were those under 18 years old, those who did not receive RA or GA, and/or had an international normalized ratio of 1.5 of greater, a partial thromboplastin time more than 35 s, or a platelet count less than 80 × 10n = 688) patients received RA only, 90.4 per cent (n = 8039) GA only, and 1.9 per cent (n = 166) both GA and RA. In the RA-only group, 91.7 per cent (631 of 688) received neuraxial anaesthesia. The primary patency rate was 93.2 per cent (573 of 615) for RA only, and 91.5 per cent (6390 of 6983) for GA only . RA was not associated with a higher rate of patency compared with GA .Included were 8893 patients with a mean(s.d.) age of 68(11) years and 31.5 per cent female. Within the cohort, 7.7 per cent  compared with general anaesthesia). Multivariable logistic regression did not show an association between anaesthetic technique and 30-day graft patency. Graft patency is associated with higher quality of life scores3; however, open lower limb revascularization is associated with a significant risk of graft failure, with 30-day patency rates ranging from 78.6 per cent for patients with acute limb ischaemia4 to 92.7 per cent in non-emergent revascularizations5. Patients with loss of graft patency may require further surgeries and/or amputation2. A regional multicentre retrospective cohort of 2036 infrainguinal bypass surgeries from 2003 to 2007 reported a 1-year permanent graft occlusion rate of 12 per cent, and 42 per cent of these patients required major amputation6.Lower limb  revascularization surgeries are performed for patients with arterial occlusion, with the goals of improving pain and function7. Rarely, a patient may receive both GA and RA, such as conversion to GA after failed RA. Clinically, major determinations for the choice between RA or GA include contraindications to regional anaesthesia (such as coagulopathy), patient preference, patient factors leading to a higher risk of complications with GA, and surgical factors including duration, complexity, and need for vein harvest from an upper extremity.Multiple anaesthesia options exist for elective infrainguinal bypass, including general anaesthesia (GA) and regional anaesthesia (RA). RA includes spinal, epidural, and combined spinal epidural anaesthesia as well as peripheral nerve blockade8, further additional evidence is required to define the relationship between anaesthetic technique and graft patency, which represents a potentially modifiable factor. Early studies suggested a possible association between neuraxial anaesthesia and decreased rates of reoperation10 and graft failure11, but subsequent studies (using data from before 2011) did not find an association between anaesthesia technique and 7-day12 or 30-day graft failure5. Plausible mechanisms by which RA may impact graft patency include sympathetic blockade, decreased catecholamine release, and improved lower limb blood flow5. In the context of arteriovenous fistula creation, regional anaesthesia is associated with improved graft patency compared with general13 or local anaesthesia14.As increasingly complex patients undergo infrainguinal revascularizationversus GA) and 30-day primary graft patency in patients undergoing elective infrainguinal bypass surgery. Secondary, aims were to analyse the relationship between anaesthetic technique and the rates of major reintervention, amputation, bleeding requiring transfusion, or secondary procedure, pneumonia, composite thromboembolism (venous thromboembolism (VTE), myocardial infarction (MI) or stroke), mortality, composite morbidity and mortality, and non-home discharge.The primary objective of this study was to determine the association between anaesthetic technique (RA ®) data set. The NSQIP general data set was linked with the ‘Vascular Procedure Targeted Datasets Lower Extremity Open (LEO)’. The NSQIP data set is a large, multicentre data set with prospectively collected variables up to 30 days after surgery15. The study was registered before undertaking the analysis ; 29 January 2021).With approval from the University of British Columbia Clinical Research Ethics Board  and a waiver of informed consent, a retrospective cohort study was performed using the National Surgical Quality Improvement Program , anaesthesia other than GA or RA , non-elective surgery status, missing procedure type, or those who did not receive a graft . Patients with a platelet count less than 80 × 109/L within 90 days before surgery16, international normalized ratio (INR) of 1.5 or higher, or partial thromboplastin time (PTT) greater than 35 s were excluded as they would likely be considered ineligible for RA based on guidelines18.All patients aged 18 years and older undergoing elective lower extremity open revascularization cases within the NSQIPAppendix S1 includes detailed definitions). RA was defined as any of spinal, epidural, and/or peripheral nerve block. In NSQIP, RA with monitored anaesthesia care (MAC) was coded as MAC for the principal technique and was included as RA for the analysis. Those receiving both GA and RA (GA + RA) were excluded from the primary analysis but included in planned sensitivity analysis.The exposure to either RA or GA was modelled as a binary variable in the primary analysis . In the LEO data set, patency documentation included imaging , physical exam, and surgeon’s diagnosis. In NSQIP, the ‘revised graft’ refers to the outcome of requiring graft revision within 30 days after the primary abstracted procedure, not whether the primary procedure itself consisted of a graft revision. NSQIP does not abstract a surgery as the primary procedure if it is performed due to a complication within 30 days of a previous procedure or the same hospitalization.All outcomes were measured within 30 days after surgery, except for hospital length of stay (LOS) which was the total LOS15, as well as a derived variable of non-home discharge (Appendix S1). Two composite secondary outcomes were created: arterial or venous thromboembolism, and composite morbidity and mortality .Secondary outcomes included major reintervention, amputation, bleeding requiring transfusion, or secondary procedure, VTE, MI or stroke, pneumonia, postoperative LOS, readmission rate, and death20, bleeding diathesis5, severe chronic obstructive pulmonary disease (COPD)20, smoking status, renal failure19, functional status6, diabetes20, total operating time19, year of surgery, high-risk physiological and anatomical risk factors as defined by NSQIP15, and procedure type (Appendix S1).In building the multivariable logistic regression model, several potential confounding variables were considered based on both previous literature and baseline differences between the two cohorts: agePatient characteristics for preoperative, intraoperative, and postoperative variables in the RA, GA, and GA + RA groups were compared with the use of descriptive statistics. Continuous variables are presented as mean(s.d.) and median (interquartile range (i.q.r.)) for parametric and non-parametric data respectively. Categorical variables are presented as frequency (per cent). Standardized mean difference or HodgesLehmann statistic was presented. Utilization of RA between 2014 and 2019 is displayed graphically. Complete case analyses without imputation for missing values were performed.P < 0.05 for all analyses. The same exposure and confounder variables were used in the multivariable logistic regressions for secondary outcomes, except that LOS was modelled by way of multivariable linear regression with logarithmic transformation of LOS due to its non-normal distribution. The degree of unmeasured confounding was assessed by way of the E value21.To assess the association among anaesthesia type and patency, a multivariable logistic regression was performed, adjusting for potential confounders as above. The significance level was set at versus femoropopliteal versus popliteal), and previous intervention using the NSQIP variable ‘high-risk factors—anatomical features’. It was not possible to perform subgroup analyses for patients receiving peripheral nerve blocks due to low frequency and lack of detailed block information within NSQIP. In another a priori sensitivity analysis, the GA + RA group was analysed as a distinct category in addition to GA-only or RA-only, as well as by grouping GA + RA into either the GA or RA group. Statistical analyses were performed with SAS 9.4 . Standardized differences were calculated with the SAS macro ‘Stddiff’22.In a planned sensitivity analysis, several subgroups were analysed: neuraxial anaesthesia compared with GA, surgical site , with an α of 0.05 and power of 0.9, it was calculated that 11 600 patients were required. Data from 2014 to 2019 were included in an attempt to achieve this sample size.Grip and colleagues previously reported a 30-day patency rate of 78.6 per cent (mean (s.d.) 1.8 per cent)Fig. 1), with a mean(s.d.) age of 68(11) years, and 31.5 per cent (2799 of 8893) were female. Variables with more than 2 per cent missing data were patency , race , additional anaesthesia technique  , preoperative PTT , and INR . The frequencies of missing data were 10.6 per cent (73 of 688) and 13.1 per cent (1056 of 6390) for the RA-only and GA-only groups respectively.The cohort included 8893 patients (n = 688) of patients received RA-only, 90.4 per cent (n = 8039) received GA-only, and 1.9 per cent (n = 166) received both GA and RA. Of those who received any RA, 85.8 per cent (733 of 854) received neuraxial anaesthesia. Of those with RA-only, neuraxial use was 91.7 per cent , and 4.4 per cent (30 of 688) received ‘regional anaesthesia’. In the GA + RA group, 61.5 per cent (102 of 166) involved neuraxial anaesthesia, with 36.7 per cent (61 of 166) receiving ‘regional anaesthesia’ (definitions in Appendix S1). Fig. 2 shows utilization of RA between 2014 and 2019.The breakdown of the cohort by anaesthesia technique is 7.7 per cent . In contrast, patients receiving GA-only were more likely to have bleeding disorders, previous intervention, longer duration of surgery, and complex grafts. The characteristics of patients in the GA + RA group are presented in Table S1.The study population characteristics are summarized in Table S2 and differed from the main cohort in terms of more asymptomatic patients and fewer previous bypasses. The patency rates for different procedure types are presented in Table 2.After excluding patients with missing patency data, the rate of patent graft for the overall cohort was 91.6 per cent (7086 of 7738). By anaesthetic technique, the patency rate was 93.2 per cent (573 of 615) for RA and 91.5 per cent (6390 of 6983) for GA . The characteristics of patients with missing data for patency are listed in P = 0.378). The model had a receiver operating characteristic (ROC) area under the curve of 0.645, with a Nagelkerke R2 of 0.0446.The use of RA only compared with GA only, was not associated with a higher rate of patency . Analyses of LOS using a Poisson regression or multivariable linear regression without log transformation produced similar results. Exploratory analysis with inclusion of ASA physical status and sex did not meaningfully alter the association between anaesthesia technique and patency, non-home discharge, or LOS.The median (i.q.r.) (range) for LOS was 4 (3–6) (0 to 35) and 4 (3–6) (0 to 88) days for RA only and GA only respectively, with a Hodges–Lehmann statistic of 0.5 days (95 per cent c.i. 0 to 1). Due to the right skewed non-normal distribution, LOS was analysed using log transformed LOS in a multivariable linear regression. The RA-only technique was associated with a 12.5 per cent increase in LOS (95 per cent c.i. 7 to 16 per cent) in the regional group  for graft failure, although this study did not adjust for important confounders such as surgical acuity and coagulopathy10. In contrast, another 2005–2008 NSQIP analysis found no difference in the secondary outcome of patency5.Primary patency rate was used as the primary outcome as it is a patient-centred outcome that is correlated with quality of lifeThe present study adds to the literature on anaesthesia type and patency by analysing an updated, contemporary cohort of patients using a specialty-focused data set. The multicentre data set has broad generalizability. The LEO data set contains more detailed clinical information that was not present in previous studies, including clear clinical, and imaging definitions of patency in the data abstraction guides. Moreover, it was possible to control for important confounders not included in some previous studies such as duration of surgery, coagulopathy, and high-risk anatomical factors.24. In terms of secondary outcomes, no association between regional technique and outcomes was found except for decreased non-home discharge and increased LOS; however, the differences were small, and these exploratory results should be interpreted with caution in the context of small sample size and multiple testing25. The difference in LOS may be related to the sicker population in the RA group, and the small magnitude of difference aligns with previous literature of small to no differences in LOS24. While RA was associated with decreased odds of non-home discharge, this may be related to the more complex grafts and surgeries in the GA group, though the mechanism remains to be elucidated given the lack of difference amongst rates of patency, reintervention, morbidity, and mortality outcomes in the current cohort. The E value of 1.6 for OR and 1.23 for lower 95 per cent c.i. limit suggest that any residual, unadjusted confounder (such as frailty) would have to have a more than small effect to change the association to null. A recent multivariable modelling of 10 145 patients in the Vascular Study Group of New England database found that preoperative predictors of non-home discharge were age, sex, non-White race, tissue loss, cardiac co-morbidity, partial ambulatory deficit, and insulin-dependent diabetes26. Whether anaesthesia technique is a potentially modifiable factor in decreasing non-home discharge should be confirmed in future studies.The literature has shown mixed results regarding the superiority of RA over GA for morbidity and mortality15. Furthermore, additional anaesthesia technique, where RA may be included, is not a mandatory variable and had a high rate of missing data (n = 8031). Reassuringly, out of 166 patients who had MAC as the principal anaesthesia technique, 139 patients had an additional anaesthesia technique of either neuraxial anaesthesia, or RA. Sensitivity analysis with the additional grouping of the GA + RA group revealed similar results. There was also a relatively high rate of missing patency values (13 per cent), which may have biased the results by excluding more patients with patent vessels who did not seek follow-up imaging. Other limitations include residual confounding by indication, and variables that were not available in the NSQIP data set, such as hospital-specific factors and anatomical details, which may affect RA utilization and outcomes27. Similarly, it was not possible to include cardiac valvular disease as a confounder20 as it was not available, though this likely did not affect the results given the less than 2 per cent incidence of heart failure in the cohort. It was also not possible to analyse outcomes beyond 30 days after surgery. Last, the study may be underpowered to detect a small difference due to a smaller than anticipated sample size.The present study should be interpreted in the context of several limitations. In many cases it can be difficult to determine the exact RA technique used due to the coding scheme and missing data. Misclassification of anaesthesia technique was possible depending on the chart documentation and the data abstracter. The principal anaesthesia technique may have been coded as GA even if there was additional RA, and MAC/intravenous sedation when this coexists with RAversus GA) and 30-day primary patency. Nevertheless, this should be interpreted with caution due to potential misclassification of anaesthesia technique within the NSQIP data set, smaller than projected sample size, and possible residual confounding. Further prospective studies would be useful to study the impact of anaesthesia technique on important patient-centred outcomes such as long-term patency and non-home discharge.For patients undergoing elective open lower limb revascularization, there was no association between the anaesthetic technique (RA zrac061_Supplementary_DataClick here for additional data file.


Cytomegalovirus (CMV) infection is a leading cause of morbidity and mortality after transplantation. This study aimed to investigate CMV seroprevalence, infection, and disease in Chinese thoracic organ transplant recipients.The clinical data of the patients who underwent lung and/or heart transplantation between January 2015 and October 2020 were retrospectively collected from four transplantation centers in China.+) recipient negative (R−) in 19 (6.17%) patients, D+/R+ in 233 (75.65%), D−/R+ in 36 (11.69%), and D−/R− in 20 (6.50%). CMV DNAemia was detected in 52.3% of the patients and tissue-invasive CMV disease was diagnosed in 16.2% of the patients. Only 31.8% of the patients adhered to the postdischarge valganciclovir therapy. The D+/R− serostatus , no valganciclovir prophylaxis , and higher doses of rabbit anti-human thymocyte globulin (> 2 mg/kg)  were risk factors of CMV disease.A total of 308 patients were analyzed. The CMV serostatus was donor positive (DCMV seroprevalence was high in Chinese thoracic organ transplant donors and recipients. The low adherence rate to the postdischarge CMV prophylaxis therapy in Chinese patients is still an unresolved issue. Organ transplantation is often the last treatment option for end-stage heart and lung disease. The number of thoracic organ transplantation is increasing rapidly in recent years, according to the International Society for Heart and Lung Transplantation registry . However−) of CMV-seropositive donors (D+) have higher risk of posttransplant CMV infection. CMV prophylaxis with antiviral agents such as valganciclovir or ganciclovir for 6 to 12 months is now a standard of care for thoracic organ transplant recipients .A total of 1800 plasma samples and 600 BALF specimens were tested. Asymptomatic CMV DNAemia was found in 161 (52.3%) recipients. Tissue-invasive CMV disease was diagnosed in 50 (16.2%) patients, consisting of 42 patients with probable CMV pneumonia, 4 with proven CMV pneumonia, and 4 with CMV gastrointestinal disease. In addition, among these patients, there were 7 patients with probable CMV retinitis, and 2 with CMV myocarditis. CMV disease was managed with intravenous ganciclovir for 2 to 3 weeks, followed by oral valganciclovir [Almost all the BALF specimens were from the lung and lung-heart recipients as bronchoscopy was not required for heart recipients if their clinical condition was stable. Among the 308 patients, CMV DNA was detected to be positive at least once in the BALF specimens in 203 (70.4%) patients.+/R− patients, 38/233 (16%) cases in the D+/R+ patients, 3/36 cases (8%) in the D−/R+ patients, and 1/20 cases (5%) in the D−R− group. Patients with CMV disease were significantly more likely to have a D+/R− serostatus (P = 0.004) and r-ATG > 2 mg/kg (P = 0.002), but were significantly less likely to have valganciclovir prophylaxis (P = 0.02) compared to those without CMV disease , and higher doses of rabbit anti-human thymocyte globulin (> 2 mg/kg)  (Table There were 8/19 42%) cases of CMV disease in the D% cases oThe median follow-up time was 18.2 months . The posttransplant 1-year all-cause mortality rate in the patients with CMV disease was significantly higher than those without it   of our patients. However, 64.4% of the specimens were from the lung transplant recipients in whom CMV pneumonia was ruled out, indicating a low specificity of detecting BALF CMV DNA in diagnosing CMV pneumonia. According to the third international consensus guidelines on the management of CMV in solid-organ transplantation –19. Howe+/R− patients constituted only 6.2% of our patients but contributed a disproportionate 16% to the patients with CMV disease. Unsurprisingly, both univariate and multivariate analyses showed that the D+/R− serostatus was a risk factor of CMV disease. Consistent with previous studies [+/R− serostatus as a primary risk factor of CMV infection in thoracic organ transplant recipients. Rapid viral DNA doubling is one of the contributing factors for the high CMV infection rate in the D+/R− recipients [The D studies –24, our cipients . ExtendeHowever, most of our patients did not adhere to the postdischarge valganciclovir therapy, with an adherence rate of 14% in the patients who developed CMV disease and an overall adherence rate of 31.8%. The duration of the postdischarge CMV prophylaxis was also relatively short. CMV prophylaxis can reduce the incidence of CMV disease , 27, whiHigher doses of r-ATG (≥ 2 mg/kg) were identified as a risk factor of CMV disease in our study. This finding was supported by previous studies showing the incidences of CMV reactivation had increased up to 10–50% since the introduction of r-ATG in solid organ transplantation –30. Howe+/R−, lack of valganciclovir prophylaxis, and poor adherence to the therapy may compromise the representativeness of our study. Thirdly, the association between valganciclovir and patient survival was not analyzed. Fourthly, plasma CMV viral load was only monitored monthly after 6 months posttransplant, which may lead to delayed detection of DNAemia.Our study has limitations. Firstly, although CMV DNA was detected in the BALF specimens in a large proportion of our patients, the association between the BALF viral loads and the incidence of CMV pneumonia was not investigated due to the retrospective nature of our study. Secondly, the small number of high-risk D+/R− serostatus. Postdischarge CMV prophylaxis was effective in reducing the incidence of CMV disease. However, its use was significantly limited in our cohort. More medical resources are needed to address this issue in the Chinese patients.The majority of the Chinese thoracic organ transplant recipients were at a moderate risk of CMV infection. However, the morbidity and mortality of CMV pneumonia in our patients were high, especially in those with the high-risk D


As the most serious complication of total knee arthroplasty (TKA), periprosthetic joint infection (PJI) often leads to disastrous consequences. An accurate preoperative diagnosis plays a significant role in saving prostheses and optimizing treatment outcomes. Through this retrospective case–control study, we aimed to investigate the potential of superoxide dismutase (SOD) as a novel serum biomarker in the diagnosis of PJI.We conducted a retrospective review of all patients who underwent TKA and received adequate follow-ups in our hospital from June 2015 to December 2021. A total of 50 patients were enrolled in the PJI group based on the 2018 International Consensus Meeting (ICM) criteria. Besides that, we enrolled 100 patients who underwent TKA in the same period and had a good postoperative course in the control group. Patient characteristics, comorbidities, laboratory results , and intraoperative findings (purulence and histopathology) were documented and compared by univariate analysis. Receiver operating characteristic (ROC) analysis was used to determine the sensitivity, specificity, and diagnostic performance.p < 0.05), respectively. With the calculated cutoff of SOD at 149.5U/L, the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were 0.919, 0.72, 0.97, 0.923, and 0.874, respectively. In subgroup analysis, the specificity of SOD in diagnosing culture-negative PJI remained extremely high (0.98). Combined diagnosis of serum SOD and C-reactive protein (CRP) made AUC increase to 0.972.The median serum SOD level in the PJI and control group was 135.95 ± 24.47 U/ml  and 173.83 ± 13.9 U/ml  (Serum SOD showed great potential in the diagnosis of PJI. Over the past half-century, total knee arthroplasty (TKA) has become one of the revolutionary advances in medicine, which can effectively relieve the pain of the patients with end-stage osteoarthritis and improve the life quality. As the most serious complication of TKA, postoperative periprosthetic joint infection (PJI) often leads to disastrous consequences, which not only bring great inconvenience to the lives of patients but also impose an enormous financial burden on the entire medical system –3. Thus,With the deepening of people’s understanding of PJI in recent years, the diagnostic methods and standards have been updated. The diagnostic criteria proposed in the 2018 International Consensus Meeting (ICM) on Musculoskeletal Infection has been widely recognized by clinicians all over the world . GuideliSerum biomarkers are the first choice for diagnosing PJI. In addition to the traditional inflammatory biomarker CRP and erythrocyte sedimentation rate (ESR), some novel serum biomarkers including fibrinogen and D-dimer have shown excellent diagnostic values. But the sensitivity and specificity still had limitations –8. NormaIn recent years, some synovial fluid biomarkers, such as white blood cell count (WBC), Polymorphonuclear% (PMN %), C-reactive protein (CRP), α-defensin, and leukocyte esterase (LE) have been regarded as effective supplements to serum inflammatory biomarkers . A meta-The microbial cultivation is also less effective than expected because it will lead to false-negative results due to antibiotic use. About 12% of culture-negative cases contain bacteria when tested by the latest molecular biological methods . BesidesFor some suspected PJI cases, it is still difficult to make an accurate preoperative diagnosis on existing laboratory biomarkers. The diagnosis largely based on intraoperative conditions and postoperative histological results. Orthopedic scholars have been trying to find more ideal laboratory biomarkers to compensate for the preoperative diagnostic evidence , 16.Some studies found the occurrence of inflammatory diseases is often accompanied by the changed activity of superoxide dismutase (SOD) , 18. SODThrough this retrospective case–control study, we aimed to investigate the potential of SOD as a novel serum biomarker in diagnosis of PJI and calculated the optimal threshold.After the institutional review board approved and waived the need to obtain informed consent, we reviewed the electronic medical record system of Tianjin Hospital and eventually completed a single-center, retrospective cohort study. The database covered all cases of knee arthroplasties and revisions performed in our hospital from June 2015 to December 2021. The information of patients in the database was kept confidential for the purpose of protecting participants’ privacy, and all methods were carried out in accordance with relevant guidelines.Patients who were scheduled to have revision surgery due to PJI were enrolled in the case group. Besides that, we selected patients who underwent TKA in the same period and had a good postoperative course in the control group according to the ratio of 1:2. The diagnosis of PJI was based on the 2018 ICM criteria . Patients who were definitely diagnosed with aseptic loosening (AL). (2).Patients with skin ulcers, hematoma, or other orthopedic infectious diseases.(3).Patients combined with autoimmune diseases, such as rheumatoid arthritis (RA), ulcerative colitis (UC), systemic lupus erythematosus (SLE).(4).Patients with malignancy or with severe liver or kidney dysfunction.(5).Patients with hematological diseases or receiving anticoagulant therapy.(6).Patients with incomplete records of laboratory information.The exclusion criteria are as follows.Exclusion criteria from (2) to (5) was considered because such diseases may affect the levels of inflammatory biomarkers, thereby interfering with the diagnostic value. The flowchart of the included and excluded patients was presented in Fig. Patient characteristics, comorbidities, laboratory results , and intraoperative findings (purulence and histopathology) were documented and compared by univariate analysis. All patients’ peripheral venous blood samples were collected on the morning of the first day after admission and were sent to the laboratory of Tianjin Hospital for testing. SOD was measured by the pyrogallol autooxidation method, and the operation steps were strictly in accordance with the standards. The SOD reference value provided by the manufacturer ranged from 129 U/ml to 216 U/ml.P < 0.05 was considered statistically significant. The receiver operating characteristic (ROC) curve was analyzed by MedCalc statistical software (version 19). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the ROC curve (AUC) were calculated and recorded in a dedicated statistical datasheet (Microsoft Excel). AUC can range between 0 and 1. The value of AUC was interpreted as excellent (0.90–1.00), good (0.80–0.89), fair (0.70–0.79), poor (0.60–0.69), or failing (0.50–0.59). The Youden index was used to determine the optimal cutoff of each biomarker. The boxplot was drawn by GraphPad Prism software (version 8).All of the statistical analyses were performed with the IBM SPSS Statistics software (version 28). Means and standard deviations were described for quantitative data and frequencies were described for qualitative data. To evaluate baseline characteristics, categorical variables were summarized using chi-squared tests, whereas the continuous variables between the 2 groups were compared with the Mann–Whitney test and a n = 50) and the non-PJI group (n = 100). In the PJI group, the patients were divided into the culture-negative group (n = 18) and the culture-positive group (n = 32) according to the results of microbial cultivation. There was no significant difference in age, BMI, and comorbidities between the two groups except for gender.Finally, the patients enrolled in this study were divided into the PJI group , respectively, while serum SOD was 135.95 ± 24.47 U/ml  and 173.83 ± 13.9 U/ml  (p < 0.05), respectively.  and 7.39 ± 9.10 mg/L   was not as obvious as that of CRP (69.84 ± 43.15 mg/L VS 47.61 ± 40.11 mg/L). , regulating immune cell function , inhibiting inflammatory mediators, and regulating cell signaling cascades  . When thOur study has some limitations. Firstly, this study is a retrospective single-center study consisting of only a small number of cases. The main reason for the small sample size is the low incidence of PJI. Large-scale multi-center data collection may make the research results more reliable. Secondly, there is no gold standard in the strict sense for diagnosing PJI at present. It may cause some selection bias, resulting in some patients with special PJI being excluded. Thirdly, preoperative use of antibiotics may affect the results of various tests. Fourthly, the low feasibility of conducting all diagnostic tests remains a major clinical concern. For instance, some synovial fluid biomarkers were not completely collected due to the limitation of our laboratory testing capability and insufficient synovial fluid sample. Although several synovial parameters which served as secondary criteria were missing, such PJI patients had sufficient scores to meet PJI diagnosis according to the ICM criteria.Serum SOD showed excellent performance in the diagnosis of PJI. Specifically, it can improve the diagnostic value combined with CRP for culture-negative PJI. As a laboratory examination with strong maneuverability and high accuracy, serum SOD is expected to be used in future clinical work. More high-quality prospective randomized controlled trials (RCTs) are needed to validate the diagnostic value of serum SOD.