Sus scrofa) breeds exhibit distinct muscle phenotypes resulting from domestication and selective breeding. Using unbiased single‐cell transcriptomic sequencing analysis (scRNA‐seq), the impact of artificial selection on cell profiles is investigated in neonatal skeletal muscle of pigs. This work provides panoramic muscle‐resident cell profiles and identifies novel and breed‐specific cells, mapping them on pseudotime trajectories. Artificial selection has elicited significant changes in muscle‐resident cell profiles, while conserving signs of generational environmental challenges. These results suggest that fibro‐adipogenic progenitors serve as a cellular interaction hub and that specific transcription factors identified here may serve as candidate target regulons for the pursuit of a specific muscle phenotype. Furthermore, a cross‐species comparison of humans, mice, and pigs illustrates the conservation and divergence of mammalian muscle ontology. The findings of this study reveal shifts in cellular heterogeneity, novel cell subpopulations, and their interactions that may greatly facilitate the understanding of the mechanism underlying divergent muscle phenotypes arising from artificial selection.Skeletal muscle comprises a large, heterogeneous assortment of cell populations that interact to maintain muscle homeostasis, but little is known about the mechanism that controls myogenic development in response to artificial selection. Different pig ( Sus scrofa) representing divergent muscle phenotypes. The findings should greatly facilitate the understanding of the mechanism of divergent muscle characteristics formation.To date, little is known about the mechanism that controls muscle phenotype modification in response to artificial selection. Using single‐cell RNA sequencing, authors delineate panoramic muscle‐resident cell profiles with identification of novel cell subpopulations and cellular interactions in pigs ( Chinese pigs have greatly contributed to global pig breeding. Originally, Chinese pigs (obese‐type) were introduced into European countries in the 18th Century to improve pig fatness, driven by demands for energy‐rich food and tallow for candle manufacturing. Chinese pigs were re‐introduced in the 1980′s to improve pig reproductive performance. During the past 60 years, domestic pigs have been subjected to intense selection for lean meat ratio and growth efficiency, driven by increasing demands for animal protein in parallel with the desire for reduced caloric intake in modern societies. Consequently, the phenotypes of skeletal muscle have undergone significant changes in terms of muscle growth and mass, myofiber‐type composition, and intramuscular fat (IMF) accumulation.The domestication of pigs . The mechanism underlying the development of these divergent muscle phenotypes is unclear.Genetic selection has led to large differences in muscle phenotypes among pig breeds. In wild boars, the original physiological characteristics of skeletal muscle have been preserved, endowing them with powerful strength, speed, stamina, and agility to survival in the wild. In contrast, Laiwu pigs, a domestic breed indigenous to China, have been selected for favorable meat quality accompanied by high fatness, with muscles characterized by relatively thin myofibers and a high level of IMF accumulation (≈10.3 ± 0.19%).5 AlteThe longissimus dorsi muscle is a representative muscle tissue for lean meat production assessment and meat quality evaluation in pig breeding. A large amount of data and knowledge based on this muscle tissue have been established, including histologic, genetic, transcriptomic, and epigenetic data concerning myogenic commitment, differentiation, and subsequent maturation.6, 7In this study, we selected the longissimus dorsi muscle to explore the shift in skeletal muscle‐resident cell profile underlying distinct muscle phenotypes resulting from domestication and artificial selection by employing scRNA‐seq analysis (scRNA‐seq). We anticipate that our findings will contribute to the current body of knowledge on the causality between muscle‐resident cell profiles and muscle phenotypes. This work also sheds light on the use of modulation of muscle‐resident cell profiles, transcriptomic expression, and ligand‐receptor interaction networks across cell types to improve muscle growth, muscle regeneration, and to develop therapeutic approaches for congenital myopathies.22.1DCN, COL1A1), but among them PPARγ was expressed at a low level; three myogenic lineages, including satellite cells (PAX7), myoblasts (MYOG), and myocytes (ACTC1 and TPM1); PDGFRb+ cells (PDGFRb and RGS5); endothelial cells (PECAM1 and CDH5); glial cells (PLP1 and MBP); myeloid cells (CSF1R and S100A8); and lymphoid cells (PTPRC and CD3G) ‐rich FAPs and myocyte‐like FAPs. The markers for each FAP subpopulation are shown in Figure PPARγ was expressed at very low levels in adipocytes and at slightly higher levels in committed preadipocytes, suggesting that PPARγ is not an appropriate marker for these two cell types in neonatal skeletal muscle of pigs.FAPs account for the largest proportion of neonatal porcine muscle cells Figure  and play8 and ND5, CYTB, COX2)  and myocyte markers   and resembles the gene expression profile of committed preadipocytes , adipogenic differentiation (cell fate B), or myogenic differentiation (cell fate C) Figure 3A, left.GLI1, IRF3, and HES1 were enriched in the tenocyte branch, while STAT1 and ID2 were identified in the adipocyte branch. These TFs are known to be important for fibrogenesis and adipogenesis, respectively. Their involvement in the activation of signaling pathways is essential for fibrogenesis  and adipogenesis . Additionally, these TFs show breed‐specific expression, indicating that they may be key regulators of FAP commitment and the divergent capacities of IMF accumulation among pig breeds.Next, we investigated key transcription factors (TFs) involved in FAP fate decision‐making  in the neonatal skeletal muscle of Laiwu pigs  ameliorates fibrosis in the liver, lungs, and other organs. Meanwhile, COL13A1+COL18A1+ tenocytes exhibited high expression of genes enriched in GO biological process in terms of nervous system development and angiogenesis. These data suggested that COL13A1+COL18A1+ tenocytes participate in the formation of the neuromuscular junction and the amelioration of fibrosis.Regarding tenocytes, the clustering distance between wild boars and Duroc pigs was the closest among the breeds Figure . We iden  specification that begins during embryogenesis. However, in our pig neonatal muscle, expression levels of MYOD1 and MRF4 differed between quiescent satellite cells  and proliferating satellite cells (satellite stem cells and PAX7+ myogenic cells). Quiescent satellite cells showed higher expression of MRF4 and lower expression of MYOD1 compared with proliferating satellite cells.Using unsupervised clustering of myogenic lineages  of each breed , and HES1+ satellite cells, which were distinguished by expression of genes that suppress myogenic differentiation . Notably, compared with other myogenic lineages, satellite stem cells exhibited strong expression (> 100‐fold) of cell cycle genes . THY1+ satellite cells expressed markers of satellite cells (PAX7) and MSCs (THY1 and CFD), representing the transitional state from MSCs to satellite cells, and were specific to wild boars.Satellite cells, myoblasts, and myocytes exhibited heterogeneity. For example, satellite cells were grouped into four subpopulations , which is consistent with the regulatory roles of calcium signaling in myoblast differentiation. Furthermore, myocytes included fast myocytes and slow myocytes with high expression of MYL1 and MYL2, respectively, corresponding to fast‐twitch and slow‐twitch fibers  appeared as a clear outgroup distinct from all other subpopulations, indicating that satellite cells were derived from the two above pathways and eventually differentiated into myoblasts (MYL1+) and myocytes (slow and fast). Moreover, the phylogenetic trees reflected that the clustering distances of each myogenic subpopulation were closer between the domestic breeds than between either domestic breed and wild boars, with the exception of myocytes.We thus constructed phylogenetic trees of the myogenic lineages by unsupervised hierarchical clustering Figure 5A, B. Fi+ satellite cells. Furthermore, myocytes in neonatal wild boars and Laiwu pigs were classified into fast and slow myocytes, while those in neonatal Duroc pigs displayed a continuum that could not be independently further classified, despite observing both fast and slow myofibers under immunofluorescence staining of muscle tissue sections , neutrophils 1 (Neu 1), neutrophils 2 (Neu 2), hematopoietic stem cells (HSCs), mast cells, monocytes, and B cells , TGFb superfamily, and TNF family members that are secreted from satellite cells, immune cells, or FAPs, regulate the proliferation  and fibrogenic  or adipogenic differentiation of FAPs  , IFN gamma (IFNG), and granulin (GRN), which regulate activities of satellite cells, immune cells, and glial cells, respectively  and cytokine  genes responsible for cell proliferation than wild boars  and interacted with pro‐NK/T cells through GPR‐19 in wild boars, indicating that adropin might play a direct regulatory role in the interaction between myocytes and immune cells. NPY stimulates the release of pro‐inflammatory mediators by mast cells in an autocrine manner. Collectively, we found more interactions of immune cells with other immune cells, committed preadipocytes (APRIL), and myocytes (ENHO) in wild boars than in domestic pigs.Compared with domestic pigs, immune cell subpopulations in wild boars showed more interactions with other cell types via a wealth of cytokines, especially within pathways involving TNF superfamily memeber 13 (APRIL), energy homeostasis associated (ENHO), and neuropeptide Y (NPY), which were specifically present in wild boars Figure . APRIL, 34 wasTo further explore the possible involvement of the cellular communication networks in the divergence of adipose phenotypes among pig breeds, we tested the roles of autocrine and paracrine mediation in FAP adipogenesis. We successfully showed that the autocrine signaling pathway, which was most intensive in Laiwu pigs , semaphorin 3 (SEMA3), and natriuretic peptide receptor 2 (NPR2), which have been positively correlated to fibrosis, were downregulated in Laiwu pigs relative to those in wild boars and Duroc pigs   Figure .To identify key signaling pathways involved in skeletal muscle evolution, gene set enrichment analysis (GSEA) was employed to compare the gene expression patterns of each homologous cell subpopulation among the breeds Figure . These d Furthermore, we showed that highly expressed genes in Duroc pigs were enriched in immune‐related categories at the single‐cell transcriptomic level, such as the B‐cell receptor signaling pathway and NK cell‐mediated cytotoxicity in PDGFRb+ cells, and the Toll‐like receptor signaling pathway in committed preadipocytes and tenocytes  and endothelial cell markers  in porcine endothelial cells and human satellite cells , mainly due to the similar expression patterns of ITGAM, SELP, ACTA2, DES, CDH15, and MET. However, in porcine endothelial cells, there was barely detectable expression of PAX7 and MYF5 and low‐level expression of MYOD, MET, CDH15, and DES. By comparison, DES was particularly highly expressed in endothelial cells from newborn wild boars. Our scRNA‐seq data implied a linkage between satellite cells and endothelial cells, which was also demonstrated in previous studies by Alfred Cossu's team.Owing to the unavailability of human and mouse data on the neonatal developmental stage, we were unable to make cross‐species comparisons at the same developmental stage. However, a cross‐species correlation analysis of whole muscle tissue indicated that similarities between our three pig breeds and humans/mice resembled those between mice and humans , we uncovered gene regulatory networks that were not previously shown to be functional in muscle development   Figure . Moreove) Figure .R2 = 0.52‐0.77). The respective correlation coefficients of pigs and mice with humans were similar in DMD  and SMA (R2 human/mouse = R2 human/pig = 0.64). In contrast, correlations of pigs and mice with human CM susceptibility genes differed , indicating that mice are more suitable than pigs as a model for the study of CM disease. Collectively, we provided a foundational set of both cross‐species and species‐specific universal markers for muscle‐resident cell types. Pigs were found to share high similarity with humans in terms of single‐cell transcriptional expression profiles, especially for the human myopathies DMD and SMA.Finally, on the basis of the hypothesis that neonatal muscle‐resident cell profiles and transcriptomes determine adult muscle characteristics, we performed a cross‐species comparison of single‐cell transcriptome profiles of skeletal muscle to examine the conservation of susceptibility genes of the following human muscle diseases: Becker muscular dystrophy (BMD), inclusion body myositis (IBM), Duchenne muscular dystrophy (DMD), myotonic dystrophy (MD), congenital myopathies (CMs), and spinal muscular atrophy (SMA)  not only include satellite stem cells but also encompass other two subpopulations with divergent myogenic potential (HES1+ and TRIB1+ satellite cells). Our study has demonstrated that the neonatal stage represents an excellent time point for revealing skeletal muscle‐resident cell profiles.One interesting finding was that neonatal skeletal muscle, in contrast to fetal and adult skeletal muscle across species,48 dis few studies have addressed the heterogeneity of FAPs in skeletal muscle. Here, we discovered several FAP subpopulations in neonatal skeletal muscle, namely MT‐rich FAPs, myocyte‐like FAPs, COL13A1+ tenocytes, and proliferating preadipocytes. These subpopulations have distinct gene expression profiles, and their roles in myogenic development should be further investigated. In addition, two FAP subpopulations (DPP4+ and CXCL14+) previously identified in homeostatic muscle were observed in our study, which we grouped as interstitial cells and committed preadipocytes, respectively.In contrast to FAPs in adipose tissue,49 few In contrast, in neonatal skeletal muscle we found that, in addition to Pro‐MACs and pro‐NK/T cells, a number of stem‐like cells, including MSCs, interstitial cells (FAP subpopulation), and HSCs (immune subpopulation), as well as proliferating preadipocytes, were in active states of proliferation and differentiation.In adult homeostatic skeletal muscle, cell turnover is limited.9c In  Considering these features of SkM.Mesen cells, namely reduced expression of myogenic markers and increased expression of mesenchymal cell markers , we regard SkM.Mesen cells as MSCs.In particular, we found a continuum between MSCs and FAPs characterized by shared canonical markers, such as CD73, CD90, and PDGFRA. In a previous study, the mesenchymal subtype of human fetal skeletal muscle (SkM.Mesen) cells expressed higher levels of mesenchymal/fibroblastic markers  than main myogenic lineages, but lower levels than mesenchymal cells, while the mesenchymal/stromal population at the postnatal stage expressed the FAP marker PDGFRA.47 ConPdgfrα, Dcn) and myogenic markers , and these fibroblasts have switched on a myogenic program and fused into the developing muscle fibers along the muscle‐tendon junctions. Interestingly, we also detected PDGFRa/PAX7 FAPs, accounting for ≈1% of total muscle‐resident FAPs, and also verified by co‐immunostaining in muscle sections. However, we do not have enough data to support the presence of this subpopulation yet, and it was s filtered out as suspected doublets by employing Scrublet.It was previously shown that a small number of dual identity cells existed at a muscle‐tendon region in P0 neonatal mice, expressing both fibrogenic identity markers (unctions.51 Int+ CD142‐like FAPs and CD9+ CD142‐like FAPs), but possess an extra transitional cell type (THY1+ satellite cells). Furthermore, we discovered breed‐specific cell types; for example, Duroc pigs possess proliferating preadipocytes and Laiwu pigs possess COL13A1+ tenocytes. Muscle tissue immune‐fluorescence staining showed that Duroc pigs have more proliferating preadipocytes in neonatal muscle than wild boars and Laiwu pigs, indicating that Duroc pigs might have the potential to develop higher IMF accumulation in future selective breeding. This possibility requires further investigation.We have also provided insights into the skeletal muscle‐resident cell profiles underpinning distinct skeletal muscle phenotypes. In contrast to domestic pigs, wild boars lack two subpopulations of FAPs  and the resulting difficulty in the required cell sorting, we did not directly examine their roles in ex vivo culture. Third, we did not profile muscle‐resident cell populations of adult pigs because it was considered a nonpriority objective.4The neonatal stage serves as a window into the panorama of muscle‐resident cells from which novel and breed‐specific cell subpopulations were identified and mapped on the pseudotime trajectory of muscle cell development. We demonstrated that artificial selection has caused significant changes in muscle‐resident cell profiles. FAPs exert key roles via ligand‐receptor interactions across cell types to form distinct muscle characteristics in pigs. Furthermore, we anticipate that this cross‐species comparison of single‐cell transcriptome data from humans, mice, and pigs will serve as a resource for the continued advancement of our knowledge of the conservation and divergence of mammalian muscle ontology.5All animal procedures followed the animal care guidelines approved by the Animal Care and Use Committee of China Agricultural University (ID: SKLAB‐B‐2010‐003).Information for all antibodies and key reagents can be found in Table Neonatal wild boars, purebred Laiwu (obese‐type), and Duroc (lean‐type) were employed in the study. Laiwu (obese‐type) and Duroc (lean‐type) piglets were purchased from the Laiwu Pig Original Breeding Farm  and Beijing Pig Breeding Center , respectively. The wild boars used for this research were obtained from the Tiancheng Farm, Yangling, Shaanxi Province, China (wild boars breeding license available). Three male piglets in each breed within 3 days‐old were selected and the three piglets of the same breed were selected from different litters.longissimus dorsi muscle were prepared by mechanical and enzymatic dissociation as previously described with minor modifications in order to minimize the enzymatic dissociation time of individual cell from the skeletal muscle tissue. Briefly, after euthanasia, 1.5 g of muscle tissue was dissociated from the longissimus dorsi muscle over the last rib, manually minced and digested with protease  for 45 min and collagenase‐type XI  for 45 min at 37 °C in a thermostatic shaker (90 r min−1). The digestion was quenched by DMEM/F12 supplemented with 10% FBS, and the supernatant was filtered successively with 100 µm and 40 µm sterile strainers . Cells were collected by centrifugation at 1000 ×g for 5 min and recovered in a growth medium.Single‐cell suspensions from the described52 witFor single‐cell sequencing, single‐cell suspensions were subjected to Debris Removal Solution , Dead Cell Removal Kit , and RBC lysing buffer (Sigma‐Aldrich) to exclude debris, dead cells, and red blood cells, as well as ambient RNA, respectively.For cell culture studies, freshly isolated single‐cell suspensions were plated in growth medium in dishes coated with collagen I (Sigma‐Aldrich) for different times to sort Adi‐lineage cells (adhesion for 2 h) and the cell supernatant was transferred to fresh culture dishes coated with collagen I and 72 h adherent Myo‐lineage cells were harvested.−1, Peptech, Burlington, MA, USA). When adi‐lineage cells reached 90% confluency, culture media was switched by adipogenic differentiation medium containing DMEM/high glucose medium, 10% FBS, insulin (10 g ml−1), 1‐methyl‐3‐isobutylmethyl‐xanthine (0.5 mM), and dexamethasone (l M), for 3 days and DMEM/high glucose medium, 10% FBS and insulin (10 g ml−1) for another 5 days to induce adipogenic differentiation. For myogenic differentiation, myo‐lineage cells were differentiated at 90% confluency in DMEM/F12 medium containing 2% horse serum for 5 days. The medium was changed every 2 days.The adi‐lineage cells and myo‐lineage cells were cultured in growth medium containing DMEM/F12 , 10% FBS , glutamine , and bFGF , OSTN , IL1A , PDGFB , or dexamethasone  dissolved in growth medium or adipogenic differentiation medium was used.For recombinant protein treatments, FGF2  in growth medium for 12 h or adipogenic differentiation medium for 72 h.CYP1B1 cDNA fragments were cloned into pcDNA3.1 vector. Plasmid transfection was performed using Lipofectamine 3000 Transfection Reagent  according to the manufacturer's instruction. After 24 h transfection, transfected myogenic precursors were switched into adipogenic differentiation medium.Cell proliferation was measured using EdU cell proliferation assay kit  according to manufacturer's instruction. To measure the proliferative capacity of Adi‐lineage and Myo‐lineage cells, the cells were added with Edu  for 2 h at 70% confluency. As for recombinant protein treatments, including stattic, vismodegib, FGF2, IL1A, OSTN, and PDGFB, adi‐lineage cells were treated with corresponding reagents above for 12 or 24 h before EdU staining. The proliferating cells were stained with Appollo in red, cell nuclear was stained with Hoechst 33 342 in blue, and cell proliferation rate was determined as the proportion of red to blue.µg was electrophoresed in 8–10% SDS‐PAGE. Western blotting analysis was performed using a standard protocol. β‐tubulin was used as a loading control. Information regarding antibodies used are listed in Table Total cell protein extracts were obtained by RIPA with protease inhibitor cocktail and phosphatase inhibitor mixture . Protein concentration was determined using a BCA protein assays kit. Protein extracts of 30 IL‐1α levels in cell culture supernatants were determined using the commercially available ELISA Kit . Colorimetric solutions were quantified using a PowerWave 340 Microplate Spectrophotometer  and ELISACalc software.longissimus dorsi muscle sections using ImageJ software.Tissues were fixed in 4% PFA, dehydrated, paraffin‐embedded, and sectioned at 3–5 µm thickness. Dewaxed sections were first microwaved in Sodium Citrate buffer (pH 6.0) and then washed with PBS and permeabilized with 0.5% Triton X‐100 for 20 min. Tissue sections were blocked in blocking buffer  at room temperature for 1 h. Primary antibodies were applied for overnight at 4 °C and fluorophore‐conjugated secondary antibodies for 1 h at room temperature. Coverslips were placed over sections with mountant and DAPI. Tissue sections were visualized with Nikon T inverted  and Olympus FluoView confocal  microscopes. For quantitative estimation, the cross‐sectional area (CSA) was calculated in the laminin‐stained The single‐cell suspension was washed twice with 1× PBS + 0.04% BSA. The cell concentration was determined using a hemocytometer and adjusted to obtain the target concentration for the 10X Chromium chip loading. Single‐cell suspensions were then loaded into the 10X Chromium Controller to generate single‐cell gel beads in emulsions (GEMs), and the Single Cell 3′ Reagent Kit v2 was applied according to the manufacturer's protocol. After the generation of GEMs, reverse transcription reactions were engaged to generate barcoded full‐length cDNA. Then cDNA was recovered, purified, and amplified to generate sufficient quantities for library preparation. Following library preparation and quantitation, the libraries were sequenced on the Illumina HiSeq xten platform.Sus scrofa transcriptome and quantify the expression of transcripts in each cell. This pipeline resulted in a raw unique molecular identifier (UMI) count matrix for each sample, which records the number of UMIs for each gene associated with each cell barcode. All cells were removed that had either fewer than 200 expressed genes or over 10% UMIs derived from the mitochondrial genome. In order to eliminate potential doublets, single cells with over 6000 genes detected were also filtered out. Finally, 60 040 single cells remained, and they were applied in downstream analyses.Cell Ranger version 3.1.0 was used to process the raw data and align the sequencing reads (fastq) to the  The “FindIntegrationAnchors” was used to identity anchors from different samples. Through these anchors, the “IntegrateData” was used to integrate the datasets together.For each sample, UMI count matrices were normalized to total cellular read count and to mitochondrial read count using linear regression as implemented in Seurat's “RegressOut” function. To eliminate batch effects among three duplicate samples of the same breed of neonatal pigs , the authors corrected for the batch effect using the “FindIntegrationAnchors” function and “IntegrateData” function in Seurat V3.53 The to predict suspected doublets and then estimated by expression patterns of cell type‐specific markers.To remove possible doublets, Scrublet was applied54 to P < 0.05 following Bonferroni correction.“LogNormalize” and “vst” were applied to normalize and find variable features within the single‐cell gene expression data. It was implemented with “FindVariableFeatures” function in the Seurat package by setting the valid value of average expression as a range from 0.05 to 5 and that of dispersion as no less than 0.5. The “RunPCA” function in the Seurat package to perform the principle component analysis (PCA) on the single‐cell expression matrix with genes restricted to highly variable genes (HVGs) was then employed. Based on the PCElbowPlot, a certain number of principal components (PCs) for the clustering analysis when that number reached to the baseline of the standard deviation of PC was picked. We then cluster the cells using the “FindClusters” function in the Seurat package. Cell clusters were visualized using t‐SNE or UMAP plots. For sub‐clustering analysis, a similar procedure was applied including the variable genes identification, dimension reduction, cell integration with Harmony, and the clustering identification to the restricted cluster derived from the overall analysis. The cell populations were annotated based on the expression pattern of differentially expressed genes (DEGs) and the well‐known cellular markers from the literature. To identify the DEGs of a cluster relative to all other clusters, the “FindAllMarkers” function in Seurat was used. Parameters provided for this function were set as following: differential expression threshold of 0.25 log fold change using Wilcoxon rank sum test with To compare similarities in cell types among the same breeds or between different breeds, average gene expression was obtained for each cluster. dist function and hclust function were used to calculate the distance and cluster the cell types respectively. And the cell‐specific expression patterns by the heat map was showed. a tool for cell type identification to predict FAP subpopulations was compared. Thereby, the similarity score of each FAPs subsets with known cell types was obtained. The reference datasets were analyzed using the Seurat pipeline. A higher score meant that the cell subpopulations were more similar to those previously known populations.The similarity of FAP subpopulations with known cell populations using SciBet (v1.0),55 a t to determine the dramatic transitional relationships among cell types and clusters. The Monocle function relative2abs was used to convert TPM measurement into mRNAs per cell, and then the CellData Set object was created with the parameter “expressionFamily = negbinomial”. The variable genes were selected using the Seurat R package. Then the cells differentiation trajectory was inferred after dimension reduction and cell ordering with the default parameters except method was specified = “DDRTree” in the reduceDimension function. The root state was set according to cell Seurat identified cell cluster label and “BEAM” function was used to calculate branch‐specific expressed genes. To plot branch‐specific expression heatmap, we used Monocle implemented “plot_genes_branched_ heatmap” function and genes with qval < 1e−4 were regarded as input genes. Genes were further divided into five sets according to k‐means. To investigate gene functions in each gene clusters, Metascape (http://metascape.org/gp/index. html#/main/step1) to perform gene ontology (GO) analysis was used.To map the differentiation of the FAP subpopulations, pseudotime analysis was performed with Monocle (v2.14)56 to + CD142‐like FAPs, MYOC+ CD142‐like FAPs, CYP1B1+ CD142‐like FAPs, interstitial cells, adipocytes, COL11A1+ tenocytes and COL13A1+ tenocytes to identify differentiation potential of CD142‐like FAP subpopulations was extracted.For Figure + myoblast and MYMK+ myoblast) and myocytes (combined myocytes (fast) and myocytes (slow)) from three pig breeds. The trajectory was inferred by the method described above.For Figure RNA velocity was performed to investigate potential inter‐relationship of myogenic lineage cells in each pig breed. BAM file containing all the myogenic cells was used in this pipeline. All the parameters were set as default. The result was visualized into UMAP plot. The single cells highly expressing G2/M‐ or S‐phase markers were scored as G2/M‐ or S‐phase cells, respectively, and the cells not expressing any of the two categories of genes were scored as G1 phase.The CellCycleScoring function in Seurat to score the cell cycle phases of each single cell was performed. This function calculated the cell cycle score based on previously published canonical marker genes.57 The in predicting ligand‐receptor interaction and cell–cell communication networks from single‐cell transcriptome data was employed. From the CellChat results, cell–cell communication in different pathways were obtained. For a given cluster, a ligand or receptor was considered expressed if 30% of cells had a UMI value of greater than 0. And the interaction across cell types in each species was shown in the heatmap.CellChat (v0.0.1)58 in The average expression for each pair of 20 homologous cell populations in wild boars, Laiwu, and Duroc pigs are calculated. Average expression values were log2‐transformed CPM. The fold difference in transcriptional expression of the homologous cells between two pig breeds was calculated for all genes and thresholds was set to identify large (>five‐fold), moderate (two to fivefold), and small (<two‐fold) differences, respectively. A heatmap was generated showing expression differences across cell types. For each of 20 homologous cell populations and three major classes of cell populations , the number of genes was quantified that had >five‐fold change in at least one cell type in that class. Scatter plots and correlations were calculated to compare the expression divergence of homologous cells between two pig breeds. Genes outside the blue lines have highly divergent expression (log2 fold change > 5) and breed‐specific marker genes were labeled in red. The BH method was employed for multiple test correction. GO terms with a P value lower than 0.05 were considered as significantly enriched  was conducted.59 The and mouse skeletal muscle scRNA‐seq data were analyzed. First, the single cell expression matrix  of human and mouse muscle tissue was downloaded, and the average homologous gene expression matrix was calculated for each species, and then the Pearson's correlations between species and pig breeds were calculated using these matrices, and results were deemed significant if the correlation P value was less than 0.01.Correlations of scRNA‐seq data of pigs against human48b anhttps://www.disgenet.org/search) across species of human, mouse and pig.It was also compared the gene expression pattern of human muscle disease (Becker Muscular Dystrophy (BMD), Congenital myopathy (CM), Inclusion Body Myositis (IBM), Muscular Dystrophy, Duchenne (DMD), Myotonic Dystrophy (MD), and Spinal Muscular Atrophy (SMA)) susceptibility genes from Disgenet dataset  for downstream analysis and comparison was used.The homologous genes of human, mouse, and pig were obtained by getLDS function in biomaRt package (v2.42.1),60 andFirst single‐cell data from human and mouse muscle tissue described above was processed. Seurat pipeline was used for dimensionality reduction and cell clustering. Then marker genes identified in the paper was used to define cell types, and the Pearson's correlation coefficients of cell types among different species were calculated and the correlations were shown in a heatmap. Crosses indicate that the corresponding cell types are the best match.Marker genes for each cell type in human, mouse, and pig muscle tissues, respectively, using “Findallmarkers” with default parameters in Seurat was also calculated. And the mouse genes were converted to human homologous genes. For the same cell type, “shared genes” meant that the genes were relatively highly expressed across three species and “species‐specific genes” meant that the genes were relatively highly expressed in one species. The ranking databases and the Motif‐to‐TF annotation database for human and mouse were downloaded from cisTarget databases (https://resources.aertslab.org/cistarget/). For pigs, the same database as for human was used. The count matrices were used as input to identify co‐expression modules by the GRNBoost2 algorithm. Following this, pySCENIC could find enriched motifs for a gene signature and optionally prune targets from this signature based on cisTarget databases. Using the aucell function, the functional activity score of the regulon is measured as the area under the recovery curve (AUC). Then, the binary regulon activity score was obtained using the binarize function. Finally, 376, 498, and 342 regulons in human, mouse, and pig was identified, respectively. Among them, there were 187 regulons were found to be overlapping between the two species from this analysis.An analysis of the regulatory network and regulatory activity of human, mouse, and pig data sets using pySCENIC (v0.10.3) was performed.61 TheP < 0.05 were considered as statistically significant. Detailed statistical methods in this paper can be found above.All statistical analyses were performed in “R” and “SAS” (version 9.2) software. Data represent means ± pooled SEMs. The significance of differences was determined using the unpaired Student's t‐test as indicated, and differences with The authors declare no conflict of interest.D.X. and B.W. contributed equally to this work. J.Y. conceptualized the project, supervised the study, and conducted quality control on the data. J.Y. and D.X. wrote the manuscript. D.X., B.W., K. Q., Y.W., X.Z., N.J., E.Y., J.W., and C.L. collected the samples. D.X.K.Q., Y.W., and R.Y. performed the experiments. D.X., J.Y., and S.G. analyzed the data. D.X., J.Y., and X.Z. assembled the figures. J.Y. and D.X. contributed to the interpretation of the results. M.L., M.D., J.W., G.F., S.G., D.X., and J.Y. discussed results and commented on the manuscripts.Supporting InformationClick here for additional data file.Supplemental Table 2Click here for additional data file.Supplemental Table 3Click here for additional data file.


The maximum bone length available for coracoid process transfer varies among individuals, while no preoperative guideline has been developed for predicting the safety margin (SM) in Latarjet shoulder reconstruction. The aim of the study was to evaluate the 3D MRI proton density (PD)‐weighted sampling perfection with application‐optimized contrasts using different flip‐angle evolution (SPACE) sequence in preoperatively predicting SM for coracoid transfer.The post‐multiplanar reconstructed images were obtained from 24 volunteers  to determine the clarity and sensitivity of the PD‐SPACE‐COR and PD‐SPACE‐FS‐COR protocols. Furthermore, the distance from the coracoid tip to the lateral edge of the attachment of trapezoid ligament (TL) was measured. To evaluate the accuracy of 3D MRI prediction, a cadaveric cohort has been launched in 32 shoulders . The distance between the tip of coracoid process and the outmost edge of TL footprint, namely, the SM, was measured.P = 0.001), and kappa coefficients . The SM determined by the PD‐SPACE‐COR protocol was 24.28 ± 2.17 mm while that by cadaveric morphometry was 25.53 ± 2.84 mm. No difference was found between measurements (P = 0.78).A better sensitivity was found in PD‐SPACE‐COR in detecting coracoclavicular ligaments (CCLs), including TL and conoid ligament (CL), compared to PD‐SPACE‐FS‐COR by ranking, McNemar test (This research provides new insights for preoperatively geometrical planning coracoid transfer by 3D MRI PD‐SPACE‐COR, which motivates personalized medicine in orthopedics. 3D MRI PD‐SPACE‐COR is qualified for preoperatively predicting safety margin in coracoid transfer, which helps optimizing the surgical decision and may motivate the development of personalized medicine in the Latarjet shoulder reconstruction surgery. Latarjet coracoid process transfer is broadly performed in treating anterior shoulder instability based on the glenoid bone loss and coracoid process dimensions.Attempts have been made to determine the bony landmark for osteotomy in Latarjet coracoid transfer using CT,The aims of this study were to: (i) assess the feasibility of 3D MRI PD‐weighted SPACE sequences in imaging the CCL attachments, including the PD‐SPACE‐COR and PD‐SPACE‐FS‐COR (fat‐suppressed) protocols; and (ii) determine the SM of the cadaveric cohort, and evaluate the reliability of PD‐SPACE‐COR in preoperatively predicting SM by comparing the 3D MRI measurement with the cadaveric morphometric data. With the potential to motivate personalized medicine, the outcomes of this study provide new insights for preoperatively geometrical planning in Latarjet shoulder reconstruction.The 3D MRI was performed on 24 volunteers  free from shoulder trauma history or participation in heavy manual labor of upper limbs. Both PD‐SPACE‐COR and PD‐SPACE‐FS‐COR protocols  and conoid ligament (CL), were taken as the criterion in evaluating the sensitivity of PD‐SPACE‐COR and PD‐SPACE‐FS‐COR protocols in CCL imaging. One radiologist (J.M.) and two orthopedic surgeons (Z.R. and H.P.) independently ranked the clarity of images from two protocols. The sensitivity was represented by percentage (%).via, Siemens, Erlangen, Germany; measurement accuracy: ± 0.1 mm) by three researchers . The data was expressed as mean ± standard deviation (range).With the post‐MPR images obtained from the 3D MRI PD‐SPACE‐COR protocol, the distance between the coracoid tip and lateral edge of the attachment of TL was identified as the predicted SM Fig.  and indeTactile measurement was performed on 32 human cadaveric shoulders  with no prior injury, surgical history, and anatomical abnormalities. The specimens were fixed with 37% formaldehyde solution, and all soft tissues  were removed to expose the coracoid process, conjoint tendon (CT), coracoacromial ligament (CAL), pectoralis minor (PM), and CCLs. The root of the coracoid process was sawn off, ligaments and tendons were removed from the coracoid process Fig. . SubsequThe SM in the specimens was identified as the distance between the tip of coracoid process and the outmost edge of TL footprint Fig. . The SM P‐values were not adjusted for multiple testing, and their interpretation was explorative. A value of P < 0.05 was considered statistically significant.SPSS 25.0  was employed. The McNemar and Cohen's kappa tests were recruited to determine the consistency between the sensitivities of two protocols. After the homogeneity of variance was determined with Levene's test, the outcomes measured by the PD‐SPACE‐COR protocol and cadaveric morphometry were compared using chi‐squared test. Moreover, the gender difference was determined using chi‐squared test. The P < 0.001) and Cohen's kappa test .The clarity of the attachments of TL and CL is good in PD‐SPACE‐COR imaging, while that of TL is not vague and CL is absent in PD‐SPACE‐FS‐COR imaging Fig. . Also, tAs it has been shown in Fig. poverall = 0.78), regardless of genders . Moreover, gender difference has been found .As it has been shown in Table In this study, the 3D MRI PD‐SPACE‐COR protocol was of a high sensitivity in CCL attachment imaging and had good accuracy in preoperatively evaluating the SM for coracoid process transfer. The inconsistent SMs between the Chinese cadaveric cohort and previous reports as well as gender difference, in turn, indicate that necessity of preoperatively geometrical planning in Latarjet shoulder reconstruction using the 3D MRI PD‐SPACE‐COR protocol.The PD‐SPACE‐COR was better than PD‐SPACE‐FS‐COR in the clarity and sensitivity of CCLs indicated by a higher ranking, significant statistic by McNemar test, and moderate disagreement by Cohen's kappa analysis.Iteratively denoised 3D SPACE allows retrospective MPRs of the osseous structures, for example, female pelvis.P = 0.27; for 25% defect, P = 0.73).r2 = 0.92) in determining glenoid bone loss.Similar to our finding, the reliability of the 3D MRI in imaging other bony and ligamentous structures has been revealed.Personalized management and precision medicine in bone and mineral disorders and bone tissue engineering have been proposed in disease state and traumatic condition.In this study, 3D MRI PD‐SPACE‐COR was qualified in predicting the SM of the coracoid process for osteotomy in Latarjet shoulder reconstruction due to its high accuracy, compared with conventional MRI and 3D CT. In a previous report, conventional MRI is limited by high slice thicknesses and large inter‐slice interval,This study suggests that 3D MRI PD‐SPACE‐COR is qualified for preoperatively predicting SM in coracoid process transfer, which optimizes the surgical decision and may motivate the development of personalized medicine in the Latarjet shoulder reconstruction surgery.Conceptualization, ZR and TY; methodology, ZR, HP and JM; data curation, ZR and HP; writing—original draft preparation, ZR and XH; writing—review and editing, ZR, XH and TY; supervision, TY and YZ; funding acquisition, TY.The authors declare no conflict of interest.This study was performed following the principles of Declaration of Helsinki. Ethical approval was granted by The Affiliated Hospital of Qingdao University .Fig. S1. Specimen preparation in cadaveric morphometric analysis. (A). Soft tissues, including skin, subcutaneous tissues, deltoid, and pectoralis major muscle, are removed to expose the coracoid process, conjoint tendon (CT), pectoralis minor (PM), coracoacromial ligament (CAL), and coracoclavicular ligament . (B). The root of the coracoid process is sawed off, ligaments and tendons were removed from the coracoid process.Click here for additional data file.Fig. S2. The images by conventional MRI (FSE‐T2WI). The attachments of the coracoclavicular ligament (CCL) was not observed in the coronal , sagittal , horizontal  planes with 3.0 mm slice thickness, 0.3 mm slice gap, 256 × 256 matrix and 20 field‐of‐view.Click here for additional data file.


Ecological interactions between parasites and their hosts play a fundamental role in evolutionary processes. Selection pressures are exerted on parasites and their hosts, usually resulting in high levels of specificity. Such is the case of ectoparasitic bat‐flies, but how large‐scale spatial gradients affect the dynamics of their interactions with their bat hosts is still unknown. In the present study, we investigated interaction patterns between bats and their ectoparasitic flies (Streblidae and Nycteribiidae), both presenting their peak of diversity in the Neotropical region, along a latitudinal gradient. Using network analyses and parasitic indices, grounded on the latitudinal diversity gradient pattern, we evaluated how spatial gradients affect species interactions and parasitic indices at the biogeopraphic scale, with increasing species richness in interaction networks closer to the tropics, leading to increases in network modularity, size, and specialization, and to a decrease in nesting and connectivity. We conducted a literature review, focusing on studies done in the Neotropical region, and own data. We obtained a bat richness of 97 species parasitized by 128 species of ectoparasitic flies, distributed into 57 interaction networks between latitudes 29° S and 19° N in the Neotropic. Network metrics and parasitic indices varied along the latitudinal gradient, with changes in the richness of bats and their ectoparasitic flies and in the structure of their interactions; network specialization, modularity, and connectance increase with latitude, while network size decreases with latitude. Regions closer to the equator had higher parasite loads. Our results show that interaction network metrics present a latitudinal gradient and that such interactions, when observed at a local scale, hide variations that only become perceptible at larger scales. In this way, ectoparasites such as bat flies are not only influenced by the ecology and biology of their hosts, but also by other environmental factors acting directly on their distribution and survival. The article brings unpublished information about the interactions and parasitic indices of bats and their flies. Showing that the specialization of interactions increases with the distance from the tropics. This is due, on the one hand, to the diversity of biological and ecological characteristics of the Chiroptera, reflected in its taxonomic and ecomorphological variety, including body size, dietary and foraging strategies, roosting ecology, social systems, and colony size, as well as to the wide geographic distribution of the group  specialization (H2): depicting the level of exclusivity of the ecological interactions, measuring niche complementarity between species, with values varying from 0 to 1, from the least to the most specialized the network . We used the structural network metrics  as response variables, and latitude as the predictor variable. We also used network size as a predictor of specialization, modularity, nestedness, and connectance to remove the effect it might have on these structural network metrics. Indeed, some metrics might be sensitive to changes in network size; for example, nestedness tends to increase with larger networks, , with 28 species of ectoparasites, followed by Carollia perspicillata , with 22 species of bat‐flies. Of all the bat species parasitized in this study, 29 were parasitized by solely one species of bat fly.We obtained data for 97 bat species, distributed in 43 genera and seven families, and 128 species of bat‐flies, 111 species and 20 genera of the Streblidae, and 17 species of the Nycteribiidae, all from the genus a Figure . The batβ = 2.298; p = .025; R2 = 0.089 – Figure β = 4.860; p < .001; R2 = 0.296 – Figure β = −8.889; p < .001; R2 = 0.677 – Figure β = −7.253; p < .001; R2 = 0.154 – Figure The specialization index (H2) was high throughout the Neotropic, ranging from 0.45 to 1.00, with the vast majority 83%) above 0.80. H2 responded positively to latitude, that is as latitude increases, networks tended to be more specialized , ranging from 0.92 to 6.12 ; data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); writing – original draft (lead); writing – review and editing . Vinicius A. G. Bastazini: Conceptualization ; funding acquisition ; methodology ; supervision (supporting); writing – review and editing . Fernando Carvalho: Writing – original draft . Maria João Ramos Pereira: Conceptualization ; formal analysis (supporting); funding acquisition ; methodology ; resources (lead); supervision (lead); writing – original draft (supporting); writing – review and editing .The authors declare no competing interests.Data S1Click here for additional data file.Data S2Click here for additional data file.Data S3Click here for additional data file.


To investigate the role of diffusion‐weighted imaging (DWI) for diagnosis and posttreatment assessment of hepatic fungal infection in patients with acute leukemia.t‐test. The ADC values of the hepatic fungal lesions of pretreatment and posttreatment were compared using paired t‐test.Patients with acute leukemia and highly suspected hepatic fungal infection were collected in the study. All the patients underwent MRI examination, including initial and follow‐up DWI. The apparent diffusion coefficient (ADC) values of the lesions and the normal liver parenchyma were compared using Student's −3 vs. 1.98 ± 0.12 × 10−3 mm2/s, p < 0.001). After treatment, the mean ADC values of the lesions were significantly increased when comparing with those of pretreatment .A total of 13 patients with hepatic fungal infections have enrolled this study. Hepatic lesions were rounded or oval shaped, measured from 0.3 to 3 cm in diameter. The lesions showed significantly hyperintense signal on DWI and markedly hypointense signal on the ADC map, reflecting a marked restricted diffusion. The mean ADC values of the lesions were significantly lower than those of normal liver parenchyma  can provide a diffusion information of hepatic fungal infection. DWI could be a valuable tool for therapy response assessment of acute leukemia. All sorts of factors include intensive chemotherapy protocols for leukemia, liver transplantation, bone marrow transplantation, prolonged antibiotic therapy, and immunosuppressive therapy, have been suggested as contributors to the increased susceptibility of hepatic fungal infections.Imaging played a key role in the diagnosis and monitoring response to the treatment of patients with clinically suspected liver infections. Both computed tomography (CT) and ultrasonography were conventional imaging modalities for detection and follow‐up of hepatic fungal infection.22.1Our institutional review board approved this study, the study approval number was JHYLS 2022073, informed consent was obtained from the participating patients. Between 2014 and 2021, 30 patients with a clinically suspected hepatic fungal infection, presented persistent fever despite apparently appropriate antibacterial therapy and abdominal pain. All patients underwent conventional MR imaging. However, 17 patients were excluded due to lacking functional DW imaging. Finally, 13 patients with conventional and DW imaging of hepatic fungal infection, performed by chemotherapy or bone marrow transplant due to acute leukemia, were retrospectively analyzed  and acute lymphoblastic leukemia (ALL)], symptoms , laboratory tests , and antifungal therapy effect (lesion absorption and disappearance).2.3b values including 50 and 1000 s/mm2). Apparent diffusion coefficient (ADC) maps were calculated automatically from all diffusion weightings on a voxel‐by‐voxel basis. Follow‐up MRI was performed for assessing response to antifungal treatment. MR images were evaluated by two radiologists  who were blinded to the clinical data, and final decisions were reached by consensus.MRI scans were performed using high‐field 3T MRI scanner . The routine MRI protocol included a breath‐hold T1‐weighted with in‐phase and out‐of‐phase sequences  and a respiratory‐triggered T2‐weighted FSE sequence . A dynamic contrast‐enhanced sequence was performed by using Gd‐DTPA (gadolinium ‐ diethylenetriamine pentaacetic acid) at a dosage of 0.01 mmol/kg during the arterial, portal venous, and equilibrium phases. Diffusion‐weighted images were acquired using an EPI sequence . One circular ROI was placed inside the lesion by adopting a manual single‐section ROI analysis  curve analysis was used to define the ADC cutoff value for differentiating normal liver parenchyma from hepatic fungal infection lesions. Differences were considered significant at p < .05 with a two‐tailed test.The analyses were performed with statistical packages . Data for categorical variables are expressed as frequency rates and percentages. Continuous variables were described using the mean, median, and interquartile range (IQR). The mean ADCs of the hepatic fungal lesions and the normal liver parenchyma were compared with Student's 33.1candida albicans in two cases and aspergillus in one case), remaining 10 patients were diagnosed as probable fungal infection. Patients were treated with antifungal drugs , ranging from 1–3 months.The demographic and clinical characteristics of the patients were shown in Table 3.2MRI of all the patients showed multiple lesions in the liver, and the lesions of four patients involved in spleen and the lesions of two patients involved in the kidneys.−3 mm2/s vs. 1.98 ± 0.12 × 10−3 mm2/s, p < 0.001)  Figure .3.3−3 mm2/s.For the differentiation between normal liver parenchyma from hepatic fungal infection lesions, ROC curve analysis revealed that ADC measurements had a diagnostic accuracy, as given by the area under the curve, of 0.970  with a sensitivity of 92.3% and a specificity of 100% using a cutoff ADC value of 1.41 × 103.4Follow‐up MRI, with a median time of 50 days, was performed for assessing antifungal therapy response. After treatment, the lesions appeared hypointense to moderate hyperintense on T1‐weighted images and isointense to moderate hyperintense on T2‐weighted images. In addition, the lesions presented mild to moderate enhanced after gadolinium‐enhancement. The lesions disappeared in five cases and absorbed in eight cases with variable degree after therapy. Of these eight cases, ADC value in six cases could be measured for quantitatively evaluating the treatment response of hepatic fungal lesion.−3 mm2/s vs. 1.06 ± 0.10 × 10−3 mm2/s, p = .016)  Figure .4In this study, we explored the value of DWI in diagnosis and therapy response assessment of hepatic fungal infection in patients with acute leukemia. We demonstrated that: 1) there were significant hyperintensities on DWI and hypointensities on ADC values in hepatic fungal lesions, when compared with normal liver parenchyma. 2) The mean ADC values of the hepatic fungal lesions in posttreatment were increased when comparing with pretreatment. To our best knowledge, our study was the first one to quantitatively evaluate diffusion features of hepatic fungal infection in patients with acute leukemia using DWI.Candida, Aspergillus, Cryptococcus neoformans, and Histoplasma capsulatum were the common pathogens of fungal infection. Lungs, liver, and spleen are the common deep organs of invasive fungal infection. Disseminated fungal disease could occur in 3%–29% of leukemia patients.Invasive fungal infection was a serious complication in patients with hematologic malignancies and transplantation.Imaging played an important role in the diagnosis and evaluation of follow‐up of hepatic fungal infection in patients with acute leukemia. In our study, conventional MRI findings of hepatic fungal infection in patients with acute leukemia were consistent with previous studies.Diffusion is the term used to describe the random (Brownian) motion of water molecules. DWI provides information of the mobility or viscosity of the water molecules in the tissue. We explored diffusion information of hepatic fungal infection using DWI. In this study, we found a marked hyperintensity on DWI and a hypointensity on ADC map in fungal hepatic abscesses in patients with acute leukemia, which could be attributed to the difference in the biochemical components of the necrotic contents. Tissues with different mobility or viscosity possess different ADC values. Hepatic abscess cavity is filled with pus, viscous fluid that consists of inflammatory cells, fungal components, and necrotic tissue with very high viscosity and cellularity, thereby resulting in significantly decreased ADC values. Thus, water diffusion in hepatic fungal infection is limited. Furthermore, we found that ADC measurements were significantly different between normal liver parenchyma from hepatic fungal infection lesions, as proved by ROC curve analysis.Moreover, we quantitatively evaluate the therapy response of hepatic fungal infection with ADC value. We found that ADC values of lesions were gradually increased with persistent antifungal treatment, suggesting low viscosity of the lesion after treatment. Therefore, ADC measurement could be used to monitor the treatment effect in patients with acute leukemia.There are some issues that need to be considered in our study. First, the sample size of this study was small, a large sample and longitudinal study need to be performed in the future. Second, this was a single‐center study, a center‐specific bias could not be excluded. However, advantages of single center study include constant work‐up routine and constant quality of MR examination.5DWI could provide diffusion information of hepatic fungal infection in patients with acute leukemia and could be taken as a valuable tool for diagnosis and therapy response assessment of these patients.Haoyu Wang: Conceptualization; writing—original draft. Haitao Yu: Formal analysis; writing—review & editing. Dong Bai: Software; writing—review & editing. Dan Yao: Writing—review & editing. Yongjun Han: Methodology; writing—review & editing. Yichao Shi: Conceptualization; writing—review & editing. Zhiqun Wang: Conceptualization; writing—review & editing.The authors declare no conflict of interest.Supporting information.Click here for additional data file.


Polygonatum odoratum is appreciated for its edible and medicinal benefits especially for lung protection. However, the contained active components have been understudied, and further research is required to fully exploit its potential application. We aimed to probe into the beneficial effects of Polygonatum odoratum polysaccharide (POP) in lipopolysaccharide‐induced lung inflammatory injury mice. POP treatment could ameliorate the survival rate, pulmonary function, lung pathological lesions, and immune inflammatory response. POP treatment could repair intestinal barrier, and modulate the composition of gut microbiota, especially reducing the abundance of Klebsiella, which were closely associated with the therapeutic effects of POP. Investigation of the underlying anti‐inflammatory mechanism showed that POP suppressed the generation of pro‐inflammatory molecules in lung by inhibiting iNOS+ M1 macrophages. Collectively, POP is a promising multi‐target microecological regulator to prevent and treat the immuno‐inflammation and lung injury by modulating gut microbiota. Polygonatum odoratum polysaccharide (POP) is a promising multi‐target microecological regulator to prevent and treat the immuno‐inflammation and lung injury by modulating gut microbiota. In addition to the ornamental qualities, it is also highly valued for its edible and medicinal properties , a main active component of P. odoratum, has been associated with antidiabetic, anti‐alcohol‐induced liver injury, and immunomodulatory effects ‐induced inflammatory mice models. By employing 16S rRNA sequencing and fecal transplantation techniques, the relationship between gut microbiota composition and lung injury‐related parameters altered by POP was directly investigated. Furthermore, the anti‐inflammatory mechanisms of POP in lung associated with gut microbiota modulation were explored by multiplex cytokine analysis and immunofluorescent assays. Our findings may offer the biological basis for clarifying the “nourishing yin and moistening dryness” effects of Polygonati Odorati Rhizoma based on the gut, and contribute to the development of clinically available anti‐immunoinflammatory and lung protective agent for lung injury treatment.22.1P. odoratum (batch NO:191201091) was purchased from Hebei Chunkai Pharmaceutical Co., Ltd, China, and authenticated by Dr. Lijuan Zhang from Tianjin University of Traditional Chinese Medicine .The material of 2.2P. odoratum was crushed into powder and refluxed twice with deionized water (liquid–solid ratio at 10:1) at 100°C for 2 h. The extracted solution (about 5 L) was pooled and further concentrated to 1/7 of the initial volume under reduced pressure at 50°C. The concentrated solution was combined and precipitated by adding 95% ethanol , and standing at 4°C for 24 h. The resultant precipitate was lyophilized to obtain the total POP powder (with the extraction rate of 12.7%). The content of polysaccharides was determined by the sulfuric acid–phenol method  were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd . All mice were housed in the SPF environment  with the free access to water and food. All animal experiments strictly complied with the Guide for the Care and Use of Laboratory Animals and approved by the Animal Ethics Committee of Tianjin University of Traditional Chinese Medicine (TCM‐LAEC2021053).2.4LPS injection induced lung injury in vivo due to a high immunoinflammatory response, and the lung injury models in this study were induced by reference to the previous study , and the receptors were treated with antibiotic cocktail of 0.5 g/L vancomycin, 1 g/L neomycin, 1 g/L metronidazole, and 1 g/L ampicillin for 3 days. Within the next 7 days, FMT group receipted intragastric administration of the supernatant from donor mice (10 μL/g) once a day.2.6The numbers of the immune cells, including the white blood cell (WBC), lymphocyte (LY), neutrophil (NE), and monocyte (MO), were tested immediately by an automated hematology analyzer (MEK‐7222K) in the blood of mice, which was collected before the euthanasia using the eyeball‐extraction method and incubated with EDTA at the room temperature. The cytokine concentrations of TNF‐α, IL‐6, and IL‐1β in serum and lung tissue, were determined by the corresponding ELISA kits (Sinobestbio), and detected by the spectrophotometry .2.7Lung, spleen, and colon tissues of mice were collected, washed with PBS, drained well with the filter paper, and then weighed. The relative weight of tissue was equal to the weight of the tissue divided by the body weight. The tissues were snap‐frozen in liquid nitrogen for further analysis. Lung and parts of the colon were fixed in 4% paraformaldehyde for 48 h, embedded in paraffin and cut into 5‐μm slices, and stained with hematoxylin–eosin (H&E). The pathological injury of lung and colon tissues was observed and imaged by the light microscope (Leica DM750).2.8The colonic and lung segments were cut out and fixed with phosphate‐buffered formalin and embedded in paraffin. Briefly, paraffin‐embedded sections of colon and lung tissue were cut into 5 μm thickness. Immunofluorescent staining was performed according to the standard instructions . The raw reads were deposited into the NCBI Sequence Read Archive database (Accession Number: SRP371245), and then analyzed according to the previous study . GraphPad Prism 7.0 was used for all statistical analyses. One‐way ANOVA was used for comparison among the multiple groups. Difference between the pairwise groups was tested for the statistical significance by the student's 33.1p < .05)  . The DEX treatment could also significantly improve the weight loss and the pulmonary function. Thus, the 400 mg/kg‐dose POP intervention not only reduced the mortality rate in mice induced by LPS, but also significantly improved the weight loss and pulmonary function, which thus provided the effectiveness and application potential of POP in the treatment of immuno‐inflammation and lung injury  Figure . Progres) Figure . As one ) Figure . Comparep < .001), while the high dose of POP treatment could decrease it from 0.0097 ± 0.00023 to 0.0084 ± 0.00018  . Given that the regulation of pro‐inflammatory cytokines is crucial to prevent excessive inflammation and tissue damage in the lungs . The high‐dose group showed the downregulated expression of IL‐6 and IL‐1β both in the lung tissue and serum .Compared with the Con group, pulmonary hemorrhage and edema were noted in the LPS‐treated mice, and treatment with high dose of POP (400 mg/kg) could obviously alleviate this phenomenon Figure . Moreove) Figure . Patholo) Figure . The rel) Figure . LPS tre) Figure . Specifi3.2Literature has reported that, compared with the Con group, LPS injection could induce disordered colon mucosa structure, crypt atrophy, and inflammatory cell infiltration . After high‐dose POP treatment for 7 days, the Chao index was significantly increased . The same enhancing trend was detected in the ACE, Shannon, and PD. Additionally, the low‐ and middle‐dose groups could also increase the gut microbial diversity, but no significant difference was observed compared with the LPS group. Principal coordinate analysis (PCoA) indicated the obvious difference between the LPS and Con samples, and the high dose of POP‐treated samples were separated from the LPS group and the other doses‐treated samples , but less Firmicutes, Bacteroidetes, and Actinobacteria (p < .001) .LPS could lead to a decreased gut microbial richness Figure . Compares Figure . Compare) Figure . The hig3.3Muribaculaceae (Muribaculaceae), norank_f_norank_o_Clostridia UCG‐014 (Clostridia UCG‐014), Lachnospiraceae NK4A136 group, norank_f_Oscillospiraceae (Oscillospiraceae), Prevotellaceae UCG‐001, norank_f_Lachnospiraceae, unclassified_f_Lachnospiraceae, and Ruminococcus was decreased; and the abundance of Klebsiella, Enterococcus, Parabacteroides, Alloprevotella, and Escherichia‐Shigella was increased after LPS modeling, which was corrected after high dose of POP 7‐day treatment , and the immunoinflammatory response, whereas, bacteria including Klebsiella, Enterococcus, and Escherichia‐Shigella showed the negative correlation . In addition, FMT also reduced the level of TNF‐α, IL‐6, and IL‐1β both in serum , and the pathological damage was reversed, which showed that the alveolar wall thickness was close to Con group, and the inflammatory cell infiltration in the lung space was reduced. FMT treatment also restored the structure of colon and reduced infiltration of inflammatory cells , suggesting a closely link with the therapeutic effects of POP ; writing – original draft (lead). Bo‐xue Chen: Data curation (supporting); writing – original draft (supporting). Mei‐ting Jiang: Investigation ; methodology . Tian‐yi Cui: Investigation ; methodology . Bin Lv: Investigation ; methodology . Zhi‐fei Fu: Investigation ; methodology . Xue Li: Investigation ; methodology . Yao‐dong Du: Visualization . Jin‐he Guo: Visualization . Xin‐qin Zhong: Visualization . Ya‐dan Zou: Visualization . Xin Zhao: Funding acquisition ; supervision ; writing – review and editing . Wen‐zhi Yang: Funding acquisition ; supervision ; writing – review and editing . Xiu‐mei Gao: Funding acquisition ; supervision .This work was supported by the National Key R&D Program of China , the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (ZYYCXTD‐C‐202009), National Natural Science Foundation of China (81872996), and Tianjin Municipal Education Commission Research Project (2022ZD039).The authors declare no conflict of interest.Data S1.Click here for additional data file.


The COVID‐19 pandemic and the implemented control measures have impacted the circulation of respiratory‐transmitted pathogens. In this report, we present data from a retrospective study that included 17,883 specimens conducted between 2018 and 2022 in our facility, describing the dynamics of circulation of the main respiratory viruses. We observed a significant decrease in all viral detections (other than SARS‐CoV‐2) starting from March 2020. However, rhinovirus maintained comparable levels to the pre‐pandemic period. Additionally, influenza viruses were not detected during the 2020–2021 season, and respiratory syncytial virus (RSV) exhibited a shift in its seasonality, with an epidemic peak occurring in the summer of 2021. Respiratory viruses typically show stable circulation and seasonality.This study aims to investigate the dynamics of the different respiratory viruses other than SARS‐CoV‐2 from March 2020 to August 2022 in Madrid, a region with a high incidence of SARS‐CoV‐2 and strong implemented control measures.2This retrospective study was conducted at Hospital Universitario 12 de Octubre, a tertiary care facility in Madrid, Spain, whose virology laboratory is a reference regional laboratory for Flu, RSV, and SARS‐CoV‐2 surveillance in primary care settings (Acute Respiratory Infection Surveillance Network). All patients attended at the emergency room (ER), clinical services, or primary care between September 1, 2018, and August 31, 2022, and tested positive for any respiratory virus other than SARS‐CoV‐2 .According to the protocols established at our center prior to the pandemic, only samples for Flu or RSV were tested during their respective seasonal circulation periods, guided by the available epidemiological data at each moment. Regarding screening for other respiratory viruses, samples were exclusively accepted from pediatric patients (≤18 years) admitted because of respiratory infections, individuals of any age with immunodeficiency conditions , or patients of all ages referred from the Primary Care of the Acute Respiratory Infection Surveillance Network.Nasopharyngeal specimens were processed by multiplex real‐time PCR using the Panther Fusion® assay . Three diagnostic panels were utilized: (1) Fusion Flu A/B/RSV, (2) Fusion AdV/hMPV/RV, and (3) Fusion Paraflu (1/2/3/4). In cases where a sample tested positive for Flu A virus, subtyping was performed using real‐time PCR with the Allplex™ assay .χ2 test, and continuous variables were analyzed using either the Student's t‐test or the Mann–Whitney U test, as appropriate.For the statistical analysis, quantitative data were presented as the median with interquartile range and qualitative variables were represented using absolute and relative frequencies. Categorical variables were compared using the 3A total of 17,883 patients were included in the study, among whom 6610 (37.0%) tested positive for a respiratory virus. The specimens underwent testing using one or more diagnostic panels based on clinical and epidemiological criteria. Specifically, 17,653 samples were analyzed using Panel 1, 7869 samples using Panel 2, and 7532 samples using Panel 3.N = 2538/17,718), followed by RV (N = 1575/7594) and RSV  . RV was involved in 79.7% of these co‐detections and was found with other viruses at various frequencies , ≥2 viruses were detected simultaneously, and this was more commonly observed in children , whereas RV was mainly identified in children (909/2971). Among pediatric patients, 50.2% were male, and among adults, 47.8%.P < 0.001) than the age observed for other viruses. When comparing the age of children with positive RSV detection before and after the pandemic, a lower median age was observed in the pre‐pandemic group  compared with the pandemic group  (P = 0.0002). Among adults, patients with RSV had an older median age of 73.6 years (IQR: 58.1–85.7) compared with other pathogens. Complete data on age distribution and the detected viruses can be found in Table Patient age varied depending on the respiratory virus identified. In children, the median age for RSV was 0.6 years IQR: 0.2–1.8), significantly lower .


Relapsed or refractory Hodgkin lymphoma (R/R HL) is a challenging disease with limited treatment options beyond brentuximab vedotin and checkpoint inhibitors. Herein we present the time‐trend analysis of R/R HL patients who received allogeneic hematopoietic cell transplantation  at our center from 2001–2017.The patients were divided into two distinct treatment cohorts: era1 (2001–2010), and era2 (2011–2017). The primary endpoint was overall survival (OS). Secondary endpoints included progression‐free survival (PFS), non‐relapse mortality (NRM), and cumulative incidence of acute and chronic graft versus host disease (GVHD).p < 0.001) and 4‐year PFS  were significantly inferior in era1 compared to era2. The incidence of 1‐year NRM was lower in era2 compared to era1 . The cumulative incidence of acute GVHD at day 100 was similar in both eras (p = 0.50), but the incidence of chronic GVHD at 1 year was higher in era2 compared to era1 .Among the 51 patients included in the study, 29 were in era1, and 22 were in era2. There was decreased use of myeloablative conditioning in era2 (18% vs. 31%) compared to era1 and 95% of patients in era2 previously received brentuximab Vedotin (BV). Haploidentical donors were seen exclusively in era2 (0% vs. 14%) and more patients received alternative donor transplants (7% vs. 32%) in era2. The 4‐year OS (34% vs. 83%, Despite the advent of novel therapies, allo‐HCT remains an important therapeutic option for patients with R/R HL. R/R HL after auto‐HCT remains a therapeutic challenge despite newer therapies. Our results show significant incremental improvements in PFS, OS, and NRM over the last two decades at our institution in allo‐HCT recipients. In a pivotal phase II trial using BV in relapse after auto‐HCT in HL, 41% of patients achieved a complete remission (CR); however, this CR was sustained only in nine patients (9% of the enrolled population) at 5 years who did not receive consolidation with allogeneic HCT  or further therapy.n = 168, MAC, 79, and RIC, 89), NRM significantly decreased in the RIC group  along with a significant improvement in overall survival (OS)  compared to the MAC cohort.n = 312, MAC = 63, and RIC = 249), MAC and RIC cohorts showed similar OS, progression‐free survival (PFS), and NRM likely reflective of better patient selection, and/or improved supportive care in recent years.Allo‐HCT has been utilized for relapsed/refractory (R/R) HL since the 1980s.While allo‐HCT is a potentially curative option in R/R HL patients, there is a paucity of data looking at the long‐term rate of utilization and outcomes following allo‐HCT, especially in the era of novel agents. Hence, we sought to evaluate the transplant characteristics, clinical outcomes, and utilization of allo‐HCT among patients with HL using data from our transplant registry.22.1This is a single‐center retrospective study of R/R HL patients receiving allo‐HCT at the Ohio State University. All patients ≥18 years of age receiving an allo‐HCT for R/R HL between 2001 and 2017 were identified from our transplant registry by using data codes for HL. Data on age, conditioning, donor source, graft versus host disease (GVHD), relapse rate, disease status at transplant, prior lines of therapy, and post‐transplant outcomes were extracted using a database query. Missing data from the database query were retrieved through medical record review. Patients were stratified into two eras  based on the date of transplant. Disease status prior to transplant was measured by PET scan in era2 while era1 utilized CT and/or PET scan. The year 2011 was chosen as the cut‐off due to the availability of BV starting that year.2) or fludarabine, cyclophosphamide, and total body irradiation (Flu/Cy/TBI). Matched related donor (MRD) recipients received tacrolimus and methotrexate (FKMTX) while matched unrelated donor (MUD) recipients received FKMTX with pre‐transplant rabbit anti‐thymocyte globulin (ATG) till 2016. All cord blood recipients received tacrolimus and mycophenolate (FKMMF) while haploidentical transplant (haplo‐HCT) recipients received FKMMF with post‐transplant cyclophosphamide (PTCy).Reduced‐intensity conditioning (RIC) regimens included fludarabine and busulfan (Flu/Bu) with a target area under the curve of 4000 uMol/min for 4 days or fludarabine and melphalan .Neutrophil recovery was defined as absolute neutrophil count (ANC) ≥500/μl for at least 3 consecutive days after post‐transplantation nadir. Platelet recovery was defined as achieving platelet counts ≥20,000/μl for at least 3 days, unsupported by transfusion. Acute GVHD and chronic GVHD were graded using standard criteria.2.3Baseline characteristics were compared across the two eras. Categorical variables are presented as counts and percentages while numeric variables were presented as a median with range. Statistical differences were determined by Chi‐Square or Fisher's exact test for categorical variables and the Wilcoxon Rank‐sum test for numeric variables. The probabilities of OS and PFS were estimated using the Kaplan–Meier method. Log‐rank test was applied to evaluate differences among the three groups. For the calculation of NRM, relapse was viewed as a competing risk. For acute and chronic GVHD, relapse, and death without GVHD were considered as competing risks. Cumulative incidence of NRM, relapse and GVHD were estimated using the cumulative incidence function and compared between groups using the Fine and Gray method. All analysis was performed in SAS software version 9.4.33.1Among the 51 patients who met the inclusion criteria, 29 patients were in era1, and 22 patients were in era2 (2011–2017). Table 3.2p = 0.08). The median time to platelet engraftment was 20 (range = 0–83), and 17 days (range = 0–46) (p = 0.21) (Table The median time to neutrophil engraftment was 13.5 (range = 0–27), and 15.5 (range = 6–19) days across eras 1 and 2, respectively (3.3The incidence of bacteremia (15%–19%) and viral reactivation of CMV, EBV, and BK virus (40–50%) within the first 100 days was not statistically different between the two eras. There were no fungal infections in either group. Only two patients had graft failure 3%), with both cases occurring in era1 . (Table ). The incidence of extensive chronic GVHD was 17% versus 45%   Table .3.6p = 0.001, Table p = 0.07), while in era2, it was NR versus 60% in the recipients of MAC versus RIC .The 1‐ and 4‐year PFS rates were 31% and 28% in era1 compared to 82% and 62% in era2 , and NRM was decreased by 5%–10% (p = 0.021) at 1 year in a pooled analysis. We also see an incremental increase in OS and decrease in NRM in era2, compared to era1, with an improvement in OS by 40% and a decline in NRM by 17% at 4 years, which is substantial and represents an improvement in supportive care and better patient selection as shown by more patients in CR at the time of transplant (31% vs. 10%) in era2 versus era1. The disease status at transplant is a well‐known prognostic factor for transplant outcomes.Allo‐HCT is a time‐tested treatment for R/R HL.Most patients in both eras received RIC conditioning, which is consistent with paradigm shift to RIC regimens nationally for patients with lymphoma undergoing allo‐HCT.With the arrival of newer therapies such as BV, CPI, and CD30 directed chimeric antigen receptor T‐cell therapy (CD30.CAR‐T), the sequencing of therapies in R/R HL is under constant evolution, and therefore, appropriate timing of allo‐HCT is not well established. In a phase I/II study, CD30.CAR‐T cell therapy was found to be highly effective with an objective response rate of 72%; however, the 1‐year PFS was only 36%.The efficacy of CPI has raised questions on the utility of allo‐HCT in HL. Although CPI provides high ORR, they have a limited duration of response. For instance, in checkmate 205, the median PFS for patients who received nivolumab was 14.7 months, and duration of response was 16.6 months.Our study is limited by the retrospective study design where the choice of therapy was at the discretion of the treating physician. Additionally, the small sample size precludes our ability to perform multivariable analysis. Lastly, the small number of patients receiving CPI peritransplant makes it difficult to deduce any conclusions on its impact on GVHD. Notwithstanding these limitations, we noted a trend of improved outcomes associated with allo‐HCT in R/R HL with a 4‐year PFS and OS of 62% and 83% in era2, despite multiple prior lines of therapy.5R/R HL after auto‐HCT remains a therapeutic challenge despite the introduction of newer therapies. Our results show significant incremental improvements in PFS, OS, and NRM over the last two decades at our institution in allo‐HCT recipients. Key factors likely contributing to the improved outcomes include improvement in NRM owing to better supportive care, RIC conditioning regimen, and Haplo/PTCy stem cell grafts, and a higher number of patients achieving chemosensitivity at the time of allo‐HCT due to the availability of more effective therapies in the relapsed setting. This data reaffirms that allo‐HCT should maintain a place in treatment algorithms even in the era of novel agents and cellular therapies.Muhammad Salman Faisal: Data curation ; writing – original draft (lead). Walter Hanel: Writing – review and editing . Timothy Joseph Voorhees: Writing – review and editing . Rui Li: Formal analysis . Ying Huang: Formal analysis . Abdullah Khan: Writing – review and editing . David Alan Bond: Writing – review and editing . Yazeed Sawalha: Writing – review and editing . John Reneau: Writing – review and editing . Lapo Alinari: Writing – review and editing . Robert Biaocchi: Writing – review and editing . Beth A Christian: Writing – review and editing . Kami Maddocks: Writing – review and editing . Yvonne Adeduni Efebera: Writing – review and editing . Sam Penza: Writing – review and editing . Ayman Saad: Writing – review and editing . Jonathan Brammer: Writing – review and editing . Marcos De Lima: Writing – review and editing . Samantha M Jaglowski: Writing – review and editing . Narendranath Epperla: Conceptualization (lead); investigation (lead); methodology (lead); writing – original draft ; writing – review and editing (lead).MSF, WH, TV, RL, YH, AK, YS, JR, LA, RB, KM, SP, JB, MD have no relevant COI. DB received consulting/ honoraria from SeaGen and Kite/Gilead. BC received research funding from Celgene, Genentech, Acerta, Triphase, MorphoSys, Merck, SeaGen, Millenium BMS; on advisory Board for Seattle Genetics, Verastem, AstraZeneca, Genentech, MorphoSys. YE is on speakers bureau for Takeda, Oncopeptide, Janssen, Alnylum; received honorarium from Takeda, Oncopeptide, Janssen, GSK, Alnylum, Sanofi; on advisory Board for Takeda, Oncopeptide, Janssen, GSK, Alnylum, Sanofi; received research support from BMS/Celgene. AS is a consultant for Magenta Therapeutics, Incyte Pharmaceuticals, CareDx; received research funding from Amgen, Kadmon, and OrcaBio. SJ received research funding from Kite, Novartis, and Caribou; on advisory board for Kite, Novartis, Juno/BMS, CRISPR, and Takeda. NE receives research funding from Beigene; Speakers Bureau: Incyte; Advisory board/Consultancy/Honorarium: TG Therapeutics, Beigene, SeaGen, Novartis.The study was approved by the Institutional review board (IRB) of Ohio State University (OSU). The study conforms to the Declaration of Helsinki.A waiver of consent was obtained from IRB at OSU. The waiver was obtained because of the retrospective nature of the study with minimal risk to the subjects and that many of the patients either passed away or had lost to follow‐up since their treatment.


Scant West African data on non‐alcoholic fatty liver disease (NAFLD) means there is little representation of this population in the modelling used to derive biomarkers and predictive indices for risk stratification of patients for the presence of hepatic steatosis. This study evaluates the performance of the fatty liver index (FLI), hepatic steatosis index (HSI) and triglyceride‐glucose (TyG) index and its derivatives in predicting ultrasound detected NAFLD in a locally resident population of Ghanaian participants.p < .05 considered statistically significant. Sensitivity, specificity, predictive values, likelihood ratios were calculated and accuracy of the proxies evaluated from area under the receiver operating characteristics curve (AUROC).A post hoc analysis of data from a cross sectional assessment of NAFLD and cardiovascular risk was performed. Data from 210 participants without significant alcohol intake, or secondary causes of fatty liver and not on steatogenic drugs was evaluated. A structured questionnaire had been used to collect demographic data, medical and drug history. Anthropometry, blood sampling for liver chemistry and fasting lipids were performed. Hepatic steatosis was detected by ultrasonography. A retrospective analysis involving multivariate binary logistic regression assessed FLI, HIS, TyG (and its derivatives) as predictors of NAFLD with p ≤ .001). All the lipid and fatty liver indices assessed performed acceptably as predictors of NAFLD. FLI , TyG‐WC  and TyG‐WHtR  performed best at predicting NAFLD. Whilst in all cases the markers had good specificity (>90%) they lacked sufficient sensitivity with FLI having the highest sensitivity of 36.7%. Their overall accuracy was greater than 70% in each case.All the biomarkers and indices were significantly associated with NAFLD  was acceptable for predicting NAFLD in this population. Given their performance in this study and in light of their low cost, accessibility, easy interpretation and non‐invasive nature; they are suitable tools for screening in the Ghanaian population. p = .001). The overall accuracy of the biomarker and indices was found to be acceptable for predicting NAFLD in this Ghanaian Population.This study evaluates the performance of the fatty liver index, hepatic steatosis index (HSI) and triglyceride‐glucose (TyG) index and its derivatives in predicting ultrasound detected NAFLD in a locally resident population of Ghanaian participants. HSI, FLI biomarkers and TyG, TyG WHtR, TyG BMI, TyG WC indices were significantly associated with NAFLD ( The scant West African data on NAFLD means there is little by way of representation of this population group in the modelling used in deriving these predictive indices. While the utility of these indices has been validated to various degrees in populations of European descentDeveloped in the Italian Dionysos nutrition and liver study,NAFLD is increasingly recognised as a growing threat in Sub‐Saharan Africa22.1This work is a sub study of a cross sectional assessment of NAFLD and cardiovascular risk carried out over a 10‐month period from April 2016 to February 2017. Utilising a consecutive sampling approach, 310 patients were consented for the study. Eighty two met one or more of the exclusion criteria. 18 failed to complete all required aspects of the study resulting in a final group of 210 recruited patients from among adult patients (>18 years) of Ghanaian descent who were attending general medical outpatient clinics at the Cape Coast Teaching Hospital, a tertiary health facility in Cape Coast, Central Region, Ghana.2.2The 210 participants had been screened for significant alcohol intake using the Alcohol Use Disorders Identification Test2.3Ethical approval for the recruitment of patients into the NAFLD and cardiovascular risk study was obtained from the Institutional Review Board of the University of Cape Coast (UCCIRB/CHAS/2015/19). Permission was also obtained from the Cape Coast Teaching Hospital for the study (CCTH/G/002/93‐15). Informed consent was obtained from all participants and documented. The recommendations guiding physicians in biomedical research involving human subjects issued by the World Medical Declaration of Helsinki (2013)2.42). Waist circumference (WC) was measured in centimetres with an inelastic tape measure at the midpoint between the lower margin of the last rib and the top of the iliac crest. Waist‐to‐height ratio (WHtR) was computed by dividing the waist by the height. Hip circumference was measured around the widest portion of the buttocks. Waist‐to‐hip ratio (WtH) was computed by dividing WC by the hip circumference.Demographic data, medical and drug histories had been obtained using a structured questionnaire. Significant use of alcohol was ruled out based on both self‐reported usage as well as the administration of the AUDIT questionnaire.2.5All study subjects had under gone phlebotomy after an overnight fast (of at least 8 hours). Hepatitis B and C serological status was ascertained by commercially available Micropoint® Hepatitis B surface antigen Gold Rapid and Micropoint® hepatitis C Gold Rapid Test kits, respectively, for the qualitative detection of hepatitis B surface antigen and antibodies to HCV in the serum. Serum was used for the measurement of transaminases and lipids in a Chemistry auto‐analyzer (ChemWell® from Awareness Technology) using Randox laboratories clinical chemistry kits. Total cholesterol analysis was carried out using an enzymatic colorimetric test, the CHOD‐PAP method, high density lipoprotein cholesterol (HDL) by precipitation method and triglyceride (TG) estimation employed enzymatic hydrolysis of TG with lipases. Low density lipoprotein cholesterol (LDL) was calculated using Friedwald's formula: LDL = ‐TG/2.2 as all the levels of TG were below 5.0 mmol/L.Abdominal ultrasound scans had been carried out by a radiologist with a Philips ClearVue 350® ultrasound machine to identify sonographic evidence of fatty liver, utilising the criteria set by Hamaguchi et al which were a brighter hepatic echo pattern in comparison to the kidneys, deep attenuation and vascular blurring; present either alone or in combination (p 2710).2.6The selected biomarkers and indices were calculated as follows.Hepatic steatosis index (HSI) = 8 × (ALT/AST ratio) + BMI* 2.72‐test was used to find associations between categorical variables. Student t‐test (independent two‐sample) was used to compare the means of scale variables with two levels of category. Cohen's d, a standardised effect size measure, was used to determine the magnitude of the difference between the means of the independent variables, with a Cohen's d ≥ 0.8 indicative of a large effect size.Data were entered in Microsoft excel® and exported to Stata®, version 15; StataCorp, software for analysis. Descriptive statistics was used to describe measures of central tendencies, frequencies and percentages. Pearson's χp ≤ .25 were considered for multivariate binary logistic regression analysis. Models were adjusted for relevant sociodemographic and other risk factor variables. A p < .05 was considered statistically significant. The accuracy, sensitivity, specificity, positive and negative predictive values as well as positive and negative likelihood ratios and AUROC were calculated for each insulin resistance index and steatosis biomarker. AUROC of ≥0.7 to <0.8 was considered acceptable, ≥0.8 to <0.9 was considered good, and ≥0.9 was considered excellent. Post hoc analyses on the fitness of the logistic regression models were conducted using the Hosmer‐Lemeshow goodness‐of‐fit test.Binary logistic regression was used to determine the relationship between relevant clinical, anthropometric parameters, insulin resistance indices and steatosis biomarkers and the presence of NAFLD. Those variables with a 33.12 (SD = 8.28), 94.34 cm (SD = 17.92) and 0.90 (SD = 0.12) respectively  as well as higher WC . The prevalence of obesity, hypertension, diabetes, dyslipidemia and metabolic syndrome among the participants was 76 (37.44%), 125 (59.52%), 35 (16.67%), 31 (14.76%) and 106 (50.47%), respectively.The 210 participants were predominantly female, with a female‐to‐male ratio of 2.6:1 with a median age of 54 years (IQR 42–61). The mean BMI, WC and waist‐to‐hip ratio of the study participants were 28.29 kg/mp < .05; Table d = .88) and WHtR (d = .98) between the groups suggested a large size effect. These differences in means were, however, not significant for laboratory characteristics like ALT, total cholesterol and HDL‐C. Participants with NAFLD had a slightly lower mean LDL‐C but this was also not statistically significant  as shown in Table A total of 52 patients (24.8%) met the ultrasonographic criteria for diagnosis of fatty liver, 10 of whom were males (17.2%) and 42 females (27.6%). There were significant differences between those with NAFLD and those without sonographic evidence of NAFLD in relation to age, waist‐to‐hip ratio, waist‐to‐height ratio, diastolic and systolic blood pressures, BMI, fasting plasma glucose (FPG), TG and HDL‐C with the NAFLD group generally having higher mean values than the non‐NAFLD group , WC (COR 11.01 95% CI [2.57–47.12]), WHtR (COR 14.75 95% CI [2.0–110.96]) and WtH ratio (COR 4.39 95% CI [1.64–11.70]) were predictors of NAFLD on bivariate analysis. In the case of WtH ratio, WHtR and WC these failed to meet the benchmark for significance after adjustment and controlling for potential confounders in multivariate models (Table ), WC (CO3.3p ≤ .001). With the exception of the triglyceride‐to‐high‐density lipoprotein cholesterol ratio, all the variables in this category demonstrated a large effect size . This is shown in Table Participants with NAFLD had significantly higher mean lipid and related metabolic indices than participants without NAFLD on ultrasonography , and TG/HDL ratio  were predictors of NAFLD in this study population, as was TyG index  although with a much greater degree of uncertainty  the logistic regression analysis demonstrated that FLI  and TG were significantly associated with NAFLD. Postulated to be driven by insulin resistance among other things,p < .001),p = .03) (Table 2 (SD ± 8.28). This may partly explain the low sensitivity of FLI in identifying those with NAFLD in this study. The NAFLD in Healthy Life in an Urban Setting (HELIUS) study suggested ethnic differences in the utility of biomarkers in detecting NAFLD, comparing participants of Ghanaian descent with those of Dutch, Moroccan and South Asian descent all living in Europe.The current study also demonstrated that the composite biomarkers  and lipid related indices  reviewed were associated with NAFLD in our study participants. Using the same cut‐offs suggested by Bedogni et al in the initial study,Several works have reviewed the performance of predictive fatty liver biomarkers for the presence of steatosis. Despite the variations in performance of these biomarkers within different studies, there are widely noted trends. A French study found a relatively poor predictive utility of some biomarkers for NAFLD as diagnosed by ultrasonography, their AUCs were 0.56 (0.48–0.64) for FLI, 0.65 (0.58–0.73) for HSI, and 0.63 (0.54–0.71) for TyG.The key limitation of this study is the use of ultrasonography for detecting NAFLD. Although it is less sensitive in detecting steatosis less than 20%, it remains the most widely utilised clinical tool in routinely assessing liver structureTo our knowledge, this study represents the first effort to assess the performance of multiple liver steatosis indices as predictors of NAFLD in a locally resident West African population. It is the first study to assess the relationship between NAFLD and combined lipid related biomarkers in the West African context. This represents the first West African effort to simultaneously assess multiple clinical and anthropometric parameters as predictors of sonographically detected NAFLD. Notwithstanding the acknowledged limitations, this study helps fill a gap in the knowledge surrounding NAFLD in Sub‐Saharan Africa and contributes to the continuing assessment of the utility of biomarkers in identifying NAFLD.5In resource limited settings such as those that prevail in Ghana and more widely across West Africa, widely available and cost‐effective biomarkers, have the potential to have a huge impact and revolutionise screening as well as follow‐up assessment for patients with significant risk factors for NAFLD. New health technologies and strategies for chronic non communicable diseases like NAFLD very often see limited evaluation of their suitability in sub Saharan populations before becoming mainstream. There is a dearth of information regarding the performance of many indices of liver steatosis in Africa, even though there is evidence of the increasing prevalence and growing impact of NAFLD. HSI, FLI, TyG index and its derivatives  were deemed to have good overall accuracy for NAFLD. Given their low cost, wide availability, easy interpretation and non‐invasive nature, we propose them as suitable tools for screening in similar populations of hospital attendants in Ghana. More work is also needed to derive biomarkers with even greater sensitivity for NAFLD in this population.A. S. Bockarie: Conceptualization (lead); data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); project administration (lead); resources (lead); supervision (lead); writing – original draft (lead); writing – review and editing . Y. A. Nartey: Investigation ; project administration ; supervision ; writing – review and editing . P. Nsiah: Data curation ; formal analysis ; investigation ; methodology ; project administration ; supervision ; writing – review and editing . E. K. M. Edzie: Investigation ; methodology ; resources ; supervision ; writing – review and editing . D. Tuoyire: Formal analysis ; methodology ; software ; writing – review and editing . S. Acquah: Formal analysis ; writing – review and editing . S. Eliason: Validation ; writing – review and editing . B. Nkum: Conceptualization ; methodology ; supervision ; validation ; writing – review and editing .All authors declare no conflict of interest.