Aging is a complex process involving transcriptomic changes associated with deterioration across multiple tissues and organs, including the brain. Recent studies using heterochronic parabiosis have shown that various aspects of aging-associated decline are modifiable or even reversible. To better understand how this occurs, we performed single-cell transcriptomic profiling of young and old mouse brains after parabiosis. For each cell type, we cataloged alterations in gene expression, molecular pathways, transcriptional networks, ligand–receptor interactions and senescence status. Our analyses identified gene signatures, demonstrating that heterochronic parabiosis regulates several hallmarks of aging in a cell-type-specific manner. Brain endothelial cells were found to be especially malleable to this intervention, exhibiting dynamic transcriptional changes that affect vascular structure and function. These findings suggest new strategies for slowing deterioration and driving regeneration in the aging brain through approaches that do not rely on disease-specific mechanisms or actions of individual circulating factors. Single-cell transcriptomic profiling of young and old mouse brains following heterochronic parabiosis shows regulation of several canonical hallmarks of aging by a shift in age-induced changes of the transcriptome in a cell-type-specific manner. The brain itself is affected substantially by aging, with processes such as cellular respiration, protein synthesis, oxidative stress, neurotransmission, myelination, neurogenesis, inflammation and blood flow being compromised3. None the less, a series of recent observations demonstrate that several aspects of aging can be delayed or even reversed by a variety of interventions, including exercise5, caloric restriction6, elimination of senescent cells8, administration of rapamycin9 or metformin10, transient cell reprogramming11 and young bone marrow transplantation12.Aging is a complicated process that is far from being completely understood, although a number of hallmarks have been recognized14. Multiple publications have led to the surprising conclusion that exposure of old mice to the young circulatory environment improves the function of various tissues and organs21, including the central nervous system (CNS)25. In the CNS specifically, studies from our lab26 and others30 have shown that circulating factors in young blood stimulate functional improvement in aged and diseased brains. Conversely, it has been shown that systemic factors in old blood drive aging phenotypes in young tissues32, including the brain. The molecular underpinnings of these changes remain to be fully elucidated, but a great deal of recent work has focused on measuring age-related changes in serum blood proteins33, primarily based on the hypothesis that function-improving factors decline with age. Although this is a plausible approach36, it ignores nonprotein factors  and factors that either do or do not change in unexpected directions with aging.One of the most robust methods of improving the function of aging tissues is that of heterochronic parabiosis, a surgical procedure whereby young and old mice are joined together so that they share a common circulatory system37, we have now quantified changes in the transcriptomes of young and old mouse brains after parabiosis. The comprehensive single-cell datasets we generated allowed us to detect brain cell types, the transcriptional states of which are affected by parabiosis. We also identified changes in inter-/intracellular molecular pathways, gene regulatory networks, cell–cell interactions and senescence. Endothelial cells (ECs) were found to be highly affected by parabiosis, exhibiting strong dynamic changes in their transcriptome, with several of these validated by orthogonal assays. Overall, this work shows that heterochronic parabiosis regulates several canonical hallmarks of aging3 by shifting aging-induced changes of the transcriptome in a cell-type-specific manner.Following our previously published work employing single-cell RNA-sequencing (scRNA-seq), which describes changes that occur in the brain during aging38  in young heterochronic versus young unpaired mice (P = 0.002)  Cs) Fig. .37 , which represent the largest fraction of ECs; ECs positive for astrocytic markers, such as Slc1a3 (EC_2)39, potentially due to the presence of adherent RNA-containing astrocytic endfeet; ECs expressing the mitogenic/neovascularization marker Lrg1 (ref. 40) (EC_3); ECs positive for the olfactory marker Omp (EC_4), possibly reflecting RNA in olfactory axons still attached to ECs; and ECs denoted by the expression of Plvap, known to be expressed in fenestrated ECs in the choroid plexus41 and circumventricular regions (EC_5). Although EC_2 has been characterized by others, EC_2 and EC_4 may be consequences of the mild dissociation protocol used  analysis to identify the gene changes that are associated with the rejuvenation process in old heterochronic parabionts or with the aging acceleration process in young heterochronic parabionts. In the analysis, we attempted to compensate for the effects of the parabiosis surgery itself. We used a pseudobulk approach  or acceleration (AGA) of aging. More specifically, the RJV DGE dataset lists the old heterochronic genes (OY) taking into account isochronic mice (OO) and are associated with a transition to a ‘more youthful’ state with respect to normal aging (OX–YX). Conversely, the aging AGA DGE dataset lists the young heterochronic genes (YO) taking into account isochronic mice (YY) and are associated with a transition to a ‘more aged’ state with respect to normal aging (YX–OX)  .47. None the less, our analysis also showed that the proportions of total DGEs per cluster that were common to aging and RJV varied from cell type to cell type, as did those that were common to aging and AGA  ≤ 0.05), whereas 442 were significantly changed in RJV and 155 in AGA (Supplementary Tables 25 and after growth differentiation factor (GDF)11 treatment26, and further emphasizes the key role that ECs play in regulating communication between blood and the brain30.On examination of the DGE datasets, a clear pattern of gene expression changes in RJV and AGA (FDR ≤ 0.05) was evident among our large cell populations  Fig. –11, poteApoe and Clu) were dysregulated, similar to findings in neurodegenerative diseases such as Alzheimer’s disease50 . Heterochronic parabiosis reversed this change in the old parabionts, bringing its transcriptional levels of Klf6 close to those seen in young ECs  (log(FC))) between RJV and AGA, further highlighting their susceptibility to the aging process and their potential for manipulation. Overall, a substantial percentage of genes with expression that was increased with aging decreased their ECs after heterochronic parabiosis. For example, the transcription factor (TF) AGA Fig. . SimilarAGA Fig. . There wn55 Fig. . The DGEECs Fig. .Fig. 5RNTaken together, our computational analysis identified numerous aging-related genes across multiple cell types, including ECs, with expression restored in old mice and/or disrupted in young mice after heterochronic parabiosis, suggesting their potential involvement in the rejuvenation and/or aging acceleration process, respectively. These data are again consistent with the notion that parabiosis is likely to act in part by regulating processes important to vascular structure and health.58 to reveal biological processes and molecular pathways associated with aging, RJV and AGA. Composite ranks were calculated for each gene based on FDR and log(FC). As with our previous study on old and young animals37, the ranking metric used yielded many significant terms for most cell types Among the key ontologies that were downregulated in aging and upregulated in RJV across multiple cell types were pathways related to mitochondrial activity, such as gene ontology (GO) oxidative phosphorylation, and GO electron transport chain, as well as oxidative stress homeostasis and metabolism pathways, such as GO detoxification, GO generation of precursor metabolites and energy and reactome metabolism of amino acids, and derivatives followed this trend Fig. . RJV-dow59.In addition to the above ontologies, ECs displayed a clear pattern of normalized enrichment score (NES) sign reversal between aging and RJV, corresponding to our DGE profiling. Changes in mitochondrial and metabolic pathways were found in RJV. Processes that were downregulated include inflammatory pathways such as hallmark tumor necrosis factor α (TNFA) signaling via nuclear factor-κB (NF-κB), and apoptosis and the senescence-associated hallmark P53 pathway. Likewise, proteostasis-associated pathways, such as Reactome HSF1 activation and GO de novo protein folding, were found to be upregulated with aging and downregulated in RJV Fig. . CollectHspa1a, which encodes a stress-inducible heat shock protein. At the DGE level, as mentioned above, Hspa1a was found to be upregulated in aging and downregulated in RJV in ECs  of higher (>1) GRN activity scores than young heterochronic animals  comprising TFs and their downstream effector genesals Fig. . Vasculapes Fig. . In old pes Fig. . These pTbx3 (refs. 62) and Foxo4 (ref. 63), a member of the FOXO family of TFs that are components of a fundamental aging regulatory pathway, Patz1, which has been implicated in regulating p53 levels and senescence in ECs64, and Arnt, which participates in aryl hydrocarbon receptor signaling and is involved in several aspects of vascular biology65  identifies TFs that are bidirectional between RJV and AGA, meaning that they have increased expression in RJV and downregulation in AGA, or vice versa. DOPA demonstrated the most extreme instances of TFs that change direction, followed by VLMCs, then ECs Fig. . Considey65 Fig. . Likewiss66 Fig. .1. Although many studies have examined the actions of blood-borne factors on CNS cells, few have looked at factors secreted by the CNS cells themselves and how they are modified by aging. The importance of such secreted factors has been shown to be dysregulated in inflammation and degeneration70. To analyze changes in intercellular communication within the brain we used CellChat71. We first measured the total number of interactions for each animal type to elucidate the number of cell–cell communication connections  as the metric for putative association. This computational analysis showed that there are certain aging-induced changes in cell–cell communication that were reversed in the old heterochronic parabionts and/or potentiated in the young heterochronic parabionts. As an example, we detailed interactions involving EC ligands, which are known to secrete signals that regulate neurogenesis72 in NRPs and, recently, were reported to be implicated in signaling networks with OLG lineage cells73. We delineated a clear pattern of connection direction reversal between aging and RJV for most ligands, as well as between RJV and AGA, with AGA exhibiting a direction similar to aging  and growth factors such as brain-derived neurotrophic factor75. Another way of investigating the data is to look for aging/RJV-dependent changes in the levels of cellular receptors. We applied this analysis to ECs, known to be highly influenced by factors found in blood and recognized to be subject to interactions with PCs and ASCs. The genes encoding EC receptors found in the cell–cell communication analysis are shown in Fig. Following our previously published approachOur computational analysis identified numerous cell–cell communication networks that are perturbed during the aging process and modified on heterochronic parabiosis, and highlighted the significance of ECs as a potential target for therapeutics, because its intercellular interactions are affected by both aging and heterochronic parabiosis.77. To explore this in more detail, we performed an implementation of GSEA with a reference gene set of literature-defined, senescence-associated genes  in the old heterochronic parabiotic brains compared with the old isochronic and old brains  by which parabiosis acts at the cellular and molecular levels to improve tissue function remains elusive. In the present study, we present a comprehensive single-cell survey of the gene expression changes that occur in the aged and young mouse brain after heterochronic parabiosis as a step toward understanding how parabiosis improves brain function.Over the past few years, many studies have demonstrated that aging is a highly dynamic and malleable process, with several types of treatment reported to rejuvenate tissues and organs and, in turn, extend organismal healthspan and lifespan82. Therefore, we cannot completely rule out shifts in the numbers of certain cell populations25. Future studies employing single-nuclei sequencing approaches83 and spatially resolved transcriptomics85 may shed more light on this matter.We first assessed the cellular complexity of the parabiotic brains and showed that cell identity and composition were largely retained. Thus, functional changes associated with parabiosis are probably due, at least substantially, to changes in the gene expression profiles of CNS cells. A limitation of the present study is that, as tissue dissociation is an inherent part of the single-cell sequencing workflow, this might have resulted in nonuniform sampling problems69, loss of proteostasis86, defects in mitochondrial dysfunction89 and cellular senescence92.We further explored the gene expression changes associated with aging and heterochronic parabiosis in all identified brain cell types to discover genes and pathways that are implicated in the rejuvenation and aging acceleration. To this end, we identified the primary cell types exhibiting these changes and revealed the common and cell-type-specific, aging-induced signatures and transcriptional programs that were rescued after parabiosis in the old brains and/or disrupted in the young brains. As corroborated by recent reports, our data provide evidence that heterochronic parabiosis effectively modulates multiple manifestations of canonical aging hallmarks, including altered intercellular communication93, as well as of the aged liver and blood tissues94, were recently reported to be reversed after heterochronic parabiosis. The reprogramming of the transcriptome presented in the present study is somewhat similar to the reprogramming recently reported to take place in rats subjected to caloric restriction95. These findings raise the exciting possibility that both interventions could promote tissue rejuvenation by mitigating the appearance of similar aging-associated epigenetic alterations, and consequently their induced transcriptional changes. Thus, future studies investigating the exact epigenetic regulators and mechanisms that are responsible for these types of changes will be of high importance96.Our analyses suggest that the modulation of the aging process is mediated by reprogramming of the associated transcriptional signatures across multiple brain cell types. In support of this notion, aging-induced changes in the epigenetic status of the aged mouse brain97. It is interesting that it can be argued that the positive effects of young blood factor exposure can overcome not only aging-driven changes in gene expression, but also processes such as systemic inflammation and stress stimulated by the parabiosis surgery itself98. Our data support the view that parabiosis also exerts effects independent of the aging process, an aspect incorporated into our analysis. Moreover, parabiosis-driven effects may be mediated through means other than reversing the effects of normal aging. In recognition of this possibility, we designated the DGEs from the chained pairwise comparisons that have no overlap with aging DGEs  and B6.SJL-Ptprca Pepcb/BoyJ male mice  were housed in the Harvard Biolabs Animal Facility under standard conditions. All experimental procedures were approved in advance by the Institutional Animal Care and Use Committee of Harvard University (AEP no. 10-23) and are in compliance with federal and state laws. On the day of sacrifice, young mice were aged 3–4 months (13–15 weeks) and old mice were 20–22 months (80–87 weeks), analogous to human ages 18–20 and 65–70 years, respectively107.C57BL/6J inbred male mice  to facilitate coordinated movement. Surgical wound clips  and absorbable sutures (Ethicon no. J385H) were then used to join the skins together. On surgery, parabionts were injected subcutaneously with the anti-inflammatory enrofloxacin (5 mg kg−1) to prevent infection and the analgesics carprofen (10 mg kg−1) and buprenorphine (0.1 mg kg−1) to manage pain (single injection every 12 h for up to 3 d post-surgery). In each injection, 0.5 ml of prewarmed 0.9% (w:v) sodium chloride was also provided to prevent dehydration. Pairs were then kept in clean cages and placed on to heating pads for up to 24 post-surgeries to maintain body temperature. Throughout the surgery and postoperative recovery, each pair was monitored continuously and potential signs of pain and distress were recorded, although several physical characteristics were also analyzed, including pair weights. After 15 d post-surgery, pairs were sedated again briefly to allow removal of the surgical wound clips and remnants of the absorbable sutures. Parabiotic pairs were maintained for 4–5 weeks  and the young (CD45.1+) parabionts is demonstrated. Specifically, in the heterochronic pairs we used young mice carrying the congenic marker CD45.1 (JAX no. 002014) and old mice carrying the congenic marker CD45.2 (JAX no. 000664), whereas in the young isochronic pairs we used mice carrying either CD45.1 or CD45.2. For the blood chimerism analysis, spleens were extracted from the mice and single cells were mechanically isolated by passing the spleen through a 40-μm filter. Erythrocytes were lysed with ACK lysis buffer  for 3 min on ice and single cells were resuspended in Hanks Balanced Salt Solution  containing 2% fetal bovine serum. Splenocytes were then filtered through a 40-µm filter and stained with an antibody cocktail , APC anti-CD45.2 , PE anti-TER-119 ) and the fixable viability dye Zombie Aqua  for 30 min on ice in the dark. Cells were then washed and fixed in 1% paraformaldehyde before analysis. Cells were gated on physical parameters to identify singlets followed by gating on the Zombie AqualowTER-119− population to identify live nonerythroid cells. These cells were subsequently gated as CD45.1+ or CD45.2+ to measure the frequency of donor-derived blood cells from one partner in the spleen of the other partner. Flow cytometry analysis was performed using a BD LSR II flow cytometer (BD Biosciences) and data were analyzed with FlowJo software (v.10). We found that the partner-derived cells represented 30–50% of splenocytes , processing one pair of mice per day, thus limiting circadian variation−1. For every sample, 17,400 cells were loaded into a Chromium Single Cell 3′ Chip (10x Genomics) and processed following the manufacturer’s instructions. ScRNA-seq libraries were prepared using the Chromium v.2 Single Cell 3′ Library and Gel Bead kit v.2 and i7 Mutiplex kit (10x Genomics). Libraries were pooled based on their molar concentrations. Pooled libraries were then loaded at 2.07 pM and sequenced on a NextSeq 500 instrument (Illumina) with 26 bases for read1, 57 bases for read2 and 8 bases for Index1. Cell Ranger (v.2.0) (10x Genomics) was used to perform sample de-multiplexing, barcode processing and single-cell gene unique molecular identifier (UMI) counting, whereas a digital expression matrix was obtained for each experiment with default parameters, mapped to the 10x reference for mm10, v.1.2.0. After the initial sequencing, the samples in each pool were re-pooled based on the actual number of cells detected by Cell Ranger  sacrificed per day as mentioned above. Briefly, after dissociation, cells were diluted in ice-cold phosphate-buffered saline (PBS) containing 0.4% bovine serum albumin at a density of 1,000 cells µl110 in R (v.3.6.1). The initial dataset contained 158,767 cells with data for 21,876 genes .2.1.900237. We identified 31 major cell types with distinct expression profiles: OPCs, OLGs, OEGs, NSCs, astrocyte-restricted precursors (ARPs), ASCs, EPCs, hypendymal cells (HypEPCs), tanycytes (TNCs), CPCs, NRPs, immature neurons (ImmNs), GABA, DOPA, GLUT, CHOL, NendCs37, ECs, PCs, VSMCs, hemoglobin-expressing vascular cells (Hb-VCs)37, VLMCs, ABCs, MGs, monocytes (MNCs), macrophages (MACs), dendritic cells (DCs), NEUT, T cells (T_cell), natural killer (NK) cells and B cells (B_cell) . For each group, we re-clustered the subcategorized cell types using top 50 PCs at resolution 5. The annotation of the subclusters was performed similar to the identification of the main cell clusters.46. Seurat objects were exported to SingleCellExperiments and reads were collapsed per animal to ‘sum’ based on ‘counts’. The ‘rejuvenation framework’ RJV follows the design contrast (OY–OX)–(OO–YX), assigning in the design matrix: OY: 1, OO: −1; OX: −1; YX: 1. The ‘aging acceleration framework’ AGA follows the design contrast (YO–YX)–(YY–OX), assigning in the design matrix: YO: 1; YY: −1; YX: −1; OX: 1. Pairwise comparisons for OXvYX, OYvOX, OYvOO, OOvOX, YOvYX, YOvYY and YYvYX were also computed. Via muscat, edgeR generates a log(FC), log(counts per min), F, p_val , p_adj.loc and p_adj.glb. We used the Benjamini–Hochberg-adjusted q value p_val.loc in all downstream thresholding. Our ability to establish a baseline level of transcription is reliant on the number of cells measured and thus larger clusters’ variation can be more adequately modeled. HypEPCs and TNCs did not contain enough cells over multiple animals to successfully derive statistics. For all mouse types, raw normalized transcript per million (TPM) values were calculated and percentage of expression per animal type. For heatmap representations, the log2(z-score) of each animal’s TPM in the rejuvenation or aging acceleration process was calculated gene-wise (by row).After initial QC preprocessing and determination of cellular identities, we utilized the muscat package (v.1.0.0) in R (v.3.6.1) to perform pseudobulk DGE analysis with edgeR’s quasi-likelihood F (QLF) test112. Using the protocol previously implemented113, for each cell population and DGE comparison, genes were ranked by multiplying −log10 with the sign of the log(FC) and converted to Homo sapiens orthologs using biomaRt (v.2.46.2). From MSigDB, we used five gene sets: Hallmark pathways, GO biological process, Kyoto Encyclopedia of Genes and Genomes (KEGG), BioCarta and Reactome. In fgsea, 1,000 permutations were performed with minimum gene set size of 15 and maximum 500. Gene sets with FDR ≤ 0.25 were considered significantly enriched. Term annotations and grouping of those overrepresenting the same pathway were derived from Cytoscape software (v.3.5.1) and the AutoAnnotate app (v.1.2) as previously described37. The NES directionality was used to collate cell-type pathways per DGE comparison. Dot-plot representations are a composite of FDR and NES.GSEA was performed with the fgsea R package (v.1.12.0)60. Each animal type was analyzed with respect to each lineage. Briefly, we used GENIE3 to identify genes that are coexpressed with TFs. Then, RCisTarget prunes these coexpression modules to create GRNs (regulons). The direct targets of each TF are found using cis-regulatory motif analysis. AUCell scores each regulon’s activity, binarized to on/off at threshold 0.7. The regulon activity per animal type per lineage is graphed with rose diagram histograms. Regulons with TFs that change log(FC) direction between RJV and AGA are identified and those regulons changing direction in at least eight cell clusters are further investigated. The magnitude of the log(FC) difference between RJV and AGA can take the form of the absolute difference, with the sign of the difference positive if rejuvenation is >0 and aging acceleration is <0, negative if aging acceleration is >0, and rejuvenation is <0 or zeroed out if they are in the same direction. Regulon matrices of each animal type across all clusters, along with row-wise regulon count, are reported.We employed SCENIC to assess GRNs and score their activity, using the R implementation (v.1.1.2-2)71. The number of interaction graphs per animal were thresholded at interactions reaching P ≤ 0.05 and graphed with netVisual_circle. Rejuvenation-associated construct graphs were the subset of unique receptor:ligand:source:target combinations of interactions occurring only in OY and YX, and aging-associated construct graphs were the same combination occurring only in YO and OX, graphed via the circlize package (0.4.13.1001)114. EC receptors in all six animal types were collapsed into a master list, with DGE Padj/log(FC) graphed via ggplot2. EC receptor graphs per animal type were constructed via CellChat netVisual_aggregate.Cell–cell communication between cell types per animal type was assessed using the CellChat tool37 using the CCInx package (0.5.1). Per-comparison plots were generated between ligand–receptor pairs using the CCInx tool.Cell–cell communication per comparison was also conducted as previously described78. Briefly, the preranked ) multiplied by the sign of the log(FC) aging, RJV and AGA gene lists were permuted 1,000× against the gene set using the fgsea algorithm implemented in ClusterProfiler (v.3.14.3). The NES and Padj (Benjamini–Hochberg) are reported.Cellular senescence was investigated using functional enrichment on preranked genes against known senescence marker genes as described in the literature115 . Gene markers from Zhao et al.43 and other sources43 were used to define arterial–venous–capillary markers. The learning rate used was 1 × 10−2, with a min_delta of 0.25 and 10 runs on a V100 graphics processing unit hosted on the FAS Cannon cluster.EC ‘zonation’ was assessed through deep learning using the CellAssign framework116. Commercially available and validated probes for Cdkn1a , Hspa1a , Klf6  and Pecam1  were utilized per the manufacturer’s instructions. For each mouse and tissue, three Bregma-matched sections were imaged. Images (four per tissue section) were acquired with a Zeiss LSM 880 Confocal Microscope with identical settings across sections and represented as maximum intensity projections of acquired confocal z-stacks. Analysis was done using a script within CellProfiler software (v.4.2.1), in which Cdkn1a, Hspa1a or Klf6 puncta with a diameter between 1 and 12 pixels located within the perinuclear space (100 pixels of DAPI-positive nuclei) were identified and quantified. Cells with two or more Pecam1+ puncta were designated Pecam1+ ECs. For Hspa1a and Cdkn1a experiments, the EC marker Pecam1 was labeled by fluorophore Atto 647, whereas target probes were labeled by Atto 488 (Hspa1a) and Atto 550 (Cdkn1a). For Klf6 experiments, Pecam1 was labeled by the fluorophore Alexa Fluor-488 whereas Klf6 was labeled by the fluorophore Atto 550. Lipofuscin granules largely associated with aged brain tissue were avoided utilizing the 1- to 12-pixel cutoff for identifying puncta. For each animal, an unstained tissue was imaged as a negative control and to assess levels of background fluorescence.RNA in situ hybridization was performed on fresh-frozen brain tissue from at least three mice for each relevant condition . For sample preparation, mice were sacrificed via cervical dislocation and the brains were rapidly extracted and embedded in optimal cutting temperature  on dry ice, and subsequently stored at −80 °C until further processing. Brains were divided into 14-μm cryostat sections and RNA in situ hybridizations were carried out using the RNA in situ Multiplex Fluorescent Manual Assay kit (Advanced Cell Diagnostics (ACD)) per the manufacturer’s instructions. Briefly, thawed sections were fixed in 4% paraformaldehyde in PBS and dehydrated in sequential incubations with ethanol, followed by a 30-min Protease IV treatment and washing in 1× PBS. Appropriate combinations of hybridization probes were incubated on tissue for 2 h at 40 °C, followed by four amplification steps. Sections were subsequently stained with DAPI and mounted with Prolong Gold mounting medium . Brain regions were selected based on areas of high expression levels of assessed examined genes, according to the Allen Brain AtlasP values for cell counts, ANOVA was conducted between animal types per cell type (rstatix 0.6.0). For validation of gene expression changes by RNA in situ hybridization, two-tailed Welch’s t-test was conducted as indicated (rstatix 0.6.0). Data distribution was assumed to be normal with equal variance, but this was not formally tested.No statistical methods were used to predetermine sample sizes; our samples sizes were determined iteratively. No randomization was performed. Data collection and analysis were not performed blind to the conditions of the experiments. Animals with low average number of genes >0 (<700), percentage mitochondria >1.5 and not having cell contribution to each cluster were assessed for exclusion. In total, five OO and one YY were removed from the dataset. Further, clusters of poor quality, over percentage mitochondria = 5%, under nFeature_RNA = 250, over nFeature_RNA = 6,000, under nCount_RNA = 200, over nCount_RNA = 30,000, fewer than 5 cells were removed , average log2(FC) (avg_log2FC), pct.1 (the percentage of cells in that cell type expressing the gene) and pct.2 . List of abbreviations for all cell types and subpopulations and major markers delineating them. The cell type abbreviations and their major markers are listed in the first tab (Cell_Type_Abbreviations_Markers). In the second tab (Cell Type Seurat Markers), the top ten (by avg_log2FC) informatically derived markers from Seurat’s FindAllMarkers for each cell type are listed, along with Wilcoxon’s rank-sum Supplementary Table 2P > 0.05, *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001).Metrics of pairwise comparison of cell types by cell number. For each pairwise comparison, per cell type, ANOVA was applied (one tailed) with no multiple comparison correction, with significance designation  reported. TPM values (‘tpm’) for each animal type, per cell type, are listed. Percentage expression (‘pct’) of each gene for each animal type, per cell type, is listed.DGE metrics of RJV, per cell type. edgeR/muscat metrics from the QLF test were computed for each cluster in a comparison, with log(FC), Supplementary Table 4P value, Benjamini–Hochberg P-value adjustment for multiple comparisons  reported. TPM values (‘tpm’) for each animal type, per cell type, are listed. Percentage expression (“pct”) of each gene for each animal type, per cell type are listed.DGE metrics of AGA, per cell type. edgeR/muscat metrics from the QLF test were computed for each cluster in a comparison, with log(FC), Supplementary Table 5P value, Benjamini–Hochberg P-value adjustment for multiple comparisons  reported. TTPM values (‘tpm’) for each animal type, per cell type, are listed. Percentage expression (‘pct’) of each gene for each animal type, per cell type are listed.DGE metrics of aging, per cell type. edgeR/muscat metrics from the QLF test were computed for each cluster in a comparison, with log(FC), Supplementary Table 6P value, Benjamini–Hochberg P-value adjustment for multiple comparisons  reported. TPM values (‘tpm’) for each animal type, per cell type, are listed. Percentage expression (‘pct’) of each gene for each animal type, per cell type are listed.DGE metrics of OYvOO, per cell type. edgeR/muscat metrics from the QLF test were computed for each cluster in a comparison, with log(FC), Supplementary Table 7P value, Benjamini–Hochberg P value adjustment for multiple comparisons  reported. TPM values (“tpm”) for each animal type, per cell type are listed. Percentage expression (‘pct’) of each gene for each animal type, per cell type are listed.DGE metrics of OYvOX, per cell type. edgeR/muscat metrics from the QLF test were computed for each cluster in a comparison, with logFC, Supplementary Table 8P value, Benjamini–Hochberg P-value adjustment for multiple comparisons  reported. TTPM values (‘tpm’) for each animal type, per cell type, are listed. Percentage expression (‘pct’) of each gene for each animal type, per cell type are listed.DGE metrics of OOvOX, per cell type. edgeR/muscat metrics from the QLF test were computed for each cluster in a comparison, with log(FC),Supplementary Table 9P value, Benjamini–Hochberg P-value adjustment for multiple comparisons  reported. TPM values (‘tpm’) for each animal type, per cell type, are listed. Percentage expression (‘pct’) of each gene for each animal type, per cell type are listed.DGE metrics of YOvYY per cell type. edgeR/muscat metrics from the QLF test were computed for each cluster in a comparison, with log(FC), Supplementary Table 10P value, Benjamini–Hochberg P-value adjustment for multiple comparisons  reported. TPM values (‘tpm’) for each animal type, per cell type, are listed. Percentage expression (‘pct’) of each gene for each animal type, per cell type are listed.DGE metrics of YOvYY, per cell type. edgeR/muscat metrics from the QLF test were computed for each cluster in a comparison, with log(FC), Supplementary Table 11P value, Benjamini–Hochberg P-value adjustment for multiple comparisons  reported. TPM values (‘tpm’) for each animal type, per cell type are listed. Percentage expression (‘pct’) of each gene for each animal type, per cell type are listed.DGE metrics of YYvYX per cell type. edgeR/muscat metrics from the QLF test were computed for each cluster in a comparison, with log(FC), Supplementary Table 12RJV and aging common DGEs and unique DGEs, per cell type. DGE FDR ≤ 0.05 genes common between RJV and aging, but also those only found in RJV and those only found in aging are listed per cell type.Supplementary Table 13AGA and aging common DGEs and unique DGEs, per cell type. DGE FDR ≤ 0.05 genes common between AGA and aging, but also those only found in AGA, and those only found in aging are listed per cell type.Supplementary Table 14DGE bidirectional DGEs between RJV and AGA. For RJV and AGA DGE genes FDR ≤ 0.05 per cell type, report the genes that are RJV up (log(FC) > 0) and AGA down (log(FC) <0), or RJV down (log(FC) < 0) and AGA up (log(FC) > 0). Count is the number of times a gene is bidirectionally expressed across cell types.Supplementary Table 15Matrices of all DGE FDR ≤ 0.05 log(FC) values across cell types. Per comparison, per gene, clusters where the gene’s significance is FDR ≤ 0.05 have their log(FC) value reported. ‘Up’ column is the sum of clusters with log(FC) > 0, ‘Down’ column is the sum of clusters with log(FC) < 0.Supplementary Table 16DGE logFC values across all cell types. Per comparison, per gene, collated log(FC) values across all clusters reporting DGE, with no thresholding. ‘Up’ column is the sum of clusters with log(FC) > 0. ‘Down’ column is the sum of clusters with log(FC) < 0.Supplementary Table 17P value for multiple comparisons ≤0.25 significant terms are collated per comparison across all cell types by NES. Pathway and process metaclasses are described in Matrices of all identified significant GSEA terms per comparison across cell types. Fast preranked GSEA (fGSEA) Benjamini–Hochberg-adjusted Supplementary Table 18SCENIC regulon matrices per animal type. Per animal type, per cell type, SCENIC regulon activity scores are reported. Column ‘counts’ is the sum of cell type that have a regulon score.Supplementary Table 19. For RJV, the set of unique source:target:receptor:ligand pairs that are found only in OY and YX combined. For AGA, the set of unique source:target:receptor:ligand pairs that are found only in YO and OX combined. YX, YY, YO, OX, OO and OY display the CellChat71 networks derived for each animal type (see details in P value are the statistical measures derived by CellChat. Ligand–receptor pairs are given interaction names and assigned to a pathway. Annotation provides the type of interaction are secreted signaling, ECM–receptor, cell–cell contact. Evidence codes and relevant PubMed IDs are provided by CellChat.Cell–cell communication networks in RJV, AGA and per animal typeSupplementary Table 2078, for use as a reference gene set to perform fGSEA. HGNC.symbol denotes Homo sapiens gene symbol, MGI.ID denotes MGI ID number, MGI.symbol is Mus musculus gene symbol, Name is the long name of the gene and Feature type denotes the type of gene or pseudogene.Literature-curated senescence-associated genes. Senescence-associated genes were curated from the literatureSupplementary Table 21P value, Benjamini–Hochberg-adjusted P value for multiple testing correction, rank and genes in the leading edge.Senescence status GSEA per comparison. For RJV, AGA and aging, fGSEA via ClusterProfiler was run against a literature-curated senescence gene set (Supplementary Table


Plasmodium infection are inconsistent. Therefore, we aimed to synthesise the overall evidence of the association between ovalocytosis and malaria infection using a meta-analysis approach. The systematic review protocol was registered with PROSPERO (CRD42023393778). A systematic literature search of the MEDLINE, Embase, Scopus, PubMed, Ovid, and ProQuest databases, from inception to 30 December 2022, was performed to retrieve studies documenting the association between ovalocytosis and Plasmodium infection. The quality of the included studies was assessed using the Newcastle–Ottawa Scale. Data synthesis included a narrative synthesis and a meta-analysis to calculate the pooled effect estimate (log odds ratios [ORs]) and 95% confidence intervals (CIs) using the random-effects model. Our database search retrieved 905 articles, 16 of which were included for data synthesis. Qualitative synthesis revealed that over half of the studies showed no association between ovalocytosis and malaria infections or severity. Furthermore, our meta-analysis demonstrated no association between ovalocytosis and Plasmodium infection . In conclusion, the meta-analysis results demonstrated no association between ovalocytosis and Plasmodium infection. Hence, the role of ovalocytosis in relation to protection against Plasmodium infection or disease severity should be further investigated in larger prospective studies.Reports of an association between ovalocytosis and protection against  Plasmodium species, including Plasmodium falciparum, P. vivax, P. malariae, P. ovale curtisi, P. ovale wallikeri, and P. knowlesi2. The parasite is transmitted between humans via the bite of the female Anopheles mosquito3. The World Health Organization estimated that there were 247 million malaria cases in 84 malaria-endemic countries in 2021, resulting in 619,000 deaths4. Plasmodium infection requires interaction with several protein receptors in the membrane of the red blood cell (RBC). The most important of these is P. falciparum erythrocyte membrane protein 1, which mediates the interaction of parasite ligands on the RBC surface5. Other important malaria parasite ligand/RBC receptors involved in RBC invasion are band 3, glycophorin A, glycophorin B, glycophorin C, Duffy antigen receptor for chemokines and semaphorin 7A6.Malaria is caused by at least six 8. Band 3 is a member of the Solute Carrier 4 family of bicarbonate transporters. It is the predominant glycoprotein of the RBC membrane, where it acts as an electroneutral anion exchanger10. A mutation in or decreased expression of the Band 3 gene has been associated with changes in RBC morphology, membrane instability, and blebbing11. In vitro studies have shown that SAO RBCs are relatively resistant to invasion by certain P. falciparum isolates13. Furthermore, an in vitro study recently revealed that P. vivax field isolates invaded SAO reticulocytes 67–71% less frequently than non-SAO reticulocytes14. Additionally, a clinical study demonstrated an association between SAO and protection against clinical infections of P. vivax and severe P. falciparum malaria15. Although ovalocytosis, particularly SAO, has been reported to confer a protective effect against malaria infection and severity, there are some inconsistencies in the association between ovalocytosis and malaria, with some studies reporting no such association17. Therefore, our systematic review and meta-analysis aimed to collate and pool the evidence of ovalocytosis-associated malaria infection and severity.Southeast Asian ovalocytosis (SAO) is an RBC disorder caused by a 27-basepair deletion in the band 3 protein gene and is characterised by the presence of macro-ovalocytes and ovalostomatocytes on a blood smear18. The systematic review protocol was registered with PROSPERO (CRD42023393778).This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelinesPlasmodium OR ‘Remittent Fever’ OR ‘Marsh Fever’ OR Paludism). The searches were performed in the English language without limitations on the publication date. A search in Google Scholar was also performed on 30 December 2022 to expand the search and ensure that relevant studies were not missed in the search of the main databases. Table A systematic search of the MEDLINE, Embase, Scopus, PubMed, Ovid, and ProQuest databases was performed from database inception to 30 December 2022 to retrieve studies reporting cases of malaria in patients with ovalocytosis. The search strategy combined search terms with Boolean operators  as follows:  AND  P: participants with and without ovalocytosis, (ii) E: malaria and (iii) O: odds/risk of malarial infection. Microscopy, rapid diagnostic tests (RDTs), or molecular methods can be used to detect Plasmodium infection. The following articles were excluded with reasons: reviews, in vitro studies, case reports or series, conference abstracts without full reports, books or book chapters, letters or news articles, articles where the outcomes of interest could not be extracted and articles for which full texts were unavailable.The population, exposure, outcome approachPlasmodium species, age range (years) of patients, characteristics of ovalocytosis, method for investigating ovalocytosis  and method for Plasmodium detection. Study selection and data extraction were independently conducted by three authors  and any discrepancies or disagreements between two authors were resolved through consensus.The studies retrieved from the database searches were managed using EndNote version 20.0  reference management software. The study selection began by removing duplicate studies, and the titles and abstracts of the remaining studies were screened. Those with irrelevant titles and abstracts were removed, and then the full-text versions of potentially relevant studies were examined. Studies were included based on pre-specified eligibility criteria, and ineligible studies were excluded with specific reasons. Finally, the following data were extracted and input into a data sheet before synthesis: first author, year of publication, study design, country, participants’ characteristics, 20. This tool was designed to evaluate the quality of non-randomised studies from three broad perspectives: study group selection, study group comparability and identification of the exposure or outcome of interest for case–control or cohort studies, respectively. Each item received 1 star in the categories of selection and outcome. For each item in the comparability category, a maximum of 2 stars was awarded.Two authors (MK and KUK) independently assessed the risk of bias of the included studies using the Newcastle–Ottawa Scale (NOS)Both qualitative and quantitative data were synthesised. The qualitative synthesis involved a narrative synthesis of the outcomes of interest from the included studies. The quantitative synthesis involved a meta-analysis approach to pool the outcomes of interest from the included studies.21. The consistency index (I2 statistics) and Chi-square (Q) test were used to assess the significance and levels of heterogeneity among the included studies. I2 values of < 25%, 25%–75% and > 75% were interpreted as low, moderate and high levels of heterogeneity, respectively22. Meanwhile, a P value of 0.1 for the Q statistic was considered an indication of significant heterogeneity among the included studies, as described previously23. If the heterogeneity of effect estimates among the included studies was observed, with a P value < 0.1 for the Q statistic or an I2 value > 25%, the pooled effect estimate was computed using the random-effects model. However, if there was no significant heterogeneity of effect estimates among the included studies, with a P value > 0.1 for the Q statistic or an I2 value < 25%, the pooled effect estimate was computed using the fixed-effects model.To synthesise the pooled log odds ratio (OR) and 95% confidence interval (CI), data in 2 × 2 tables Table  were com24. The trim-and-fill method was used to correct for publication bias25, if any. Meta-regression analysis based on study design, country, participants’ characteristics, Plasmodium species, method used to investigate ovalocytosis, and method for malaria detection was performed to assess the impact of these parameters on the pooled effect estimate. If the meta-regression analysis showed a significant impact of covariates on the pooled effect estimate, a subgroup analysis was conducted to determine the difference in the pooled effect estimate between subgroups. The leave-one-out sensitivity analysis was performed to test whether a single study affected the overall pooled estimate. All analyses were performed using the meta command in Stata v17 software .The publication bias was assessed by visualisation of the funnel plot asymmetry and further validated using Egger’s test38 were included for qualitative and quantitative data synthesis , Embase n = 160), Scopus (n = 172), PubMed (n = 114), Ovid (n = 295), and ProQuest (n = 76). After removing duplicate records (n = 311), the titles and abstracts of the remaining records were screened (n = 594) to exclude non-relevant records (n = 443). Then, the remaining records (n = 151) were assessed for full-texts and eligibility. After applying the exclusion criteria, 133 records were excluded with specific reasons: reviews (n = 68), in vitro studies (n = 30), case reports (n = 10), gene mutation or polymorphism (n = 8), no abstract available (n = 4), conference abstracts (n = 2), books (n = 2), no information on ovalocytosis (n = 2), letters or news article (n = 2), inability to extract data (n = 2), unavailability of full text (n = 1), guideline (n = 1) and duplicate article (n = 1). The searches in Google Scholar identified 876 articles, none of which met the eligibility criteria. Finally, 16 studies60, Scopu37. Study designs included cross-sectional studies (50.0%)37, case–control studies (18.8%)29, cohort studies (25.0%)38 and both cohort and case–control studies (6.3%)15. Most studies were conducted in Papua New Guinea (68.8%)38, with the remainder in Indonesia (18.8%)37, Malaysia (6.3%)30 and Thailand (6.3%)27. Most studies enrolled patients infected with single or mixed infections of P. falciparum and P. vivax (56.3%)37, single or mixed infections of P. falciparum, P. vivax and P. malariae (18.8%)36, and infection with P. falciparum only (12.5%)35. The majority of study participants were children (50.0%)37, followed by pregnant women (18.8%)38. Most of the included studies tested for SAO using a polymerase chain reaction (PCR) assay to identify band 3 deletions (75.0%)38, and the remainder investigated ovalocytosis using microscopy (25.0%)36. Malaria parasites were mainly detected using microscopy (68.8%)36, followed by a combination of microscopy with PCR (18.8%)38, a combination of microscopy with RDT (6.3%)33 or PCR only (6.3%)37  published between 2000 and 201029 and three of the cohort studies33 scored the maximum of 9/9 stars. Two studies38 scored 8/9 stars: one did not demonstrate the outcome of interest at the start of the study35 and the other lacked selection of the non-exposed cohort38. 'The NOS scale for case–control studies was adapted to assess the risk of bias in cross-sectional studies. Six cross-sectional studies scored 8/8 stars36, and two37 scored 7/8 stars because they lacked a ‘definition of controls’ . Benet et al.28 reported that severe malaria infections in placental tissues were less common in pregnant women with SAO than in pregnant women in the control group. Cattani et al.29 reported that patients with ovalocytosis had a lower infection rate of P. falciparum (P = 0.044), P. vivax (P = 0.009) and all species of malaria combined (P = 0.013) compared with those without ovalocytosis. Foo et al.30 demonstrated fewer ever-positive ovalocytes for P. falciparum (P < 0.05) or any parasite species (P < 0.05) compared with the controls. Additionally, malaria parasitaemia was lower in individuals with ovalocytes compared with the controls. Rosanas-Urgell et al.15 reported that SAO was associated with a statistically significant reduction of 46% in the incidence of clinical P. vivax episodes and a 52% reduction in P. vivax blood-stage reinfection. Furthermore, in the case–control study, SAO was associated with protection against severe P. falciparum malaria  but not with protection against uncomplicated P. falciparum malaria. Allen et al.26 reported the absence of SAO band 3 in 68/68 (100%) of children with cerebral malaria compared with 6/68 (8.8%) matched community controls . Serjeantson et al.36 suggested that patients with ovalocytosis were more resistant to severe malaria than those with normocytes because the ratio of parasitaemia in 112 children with ovalocytes compared with 741 children with normocytes was 1.05 for P. falciparum; 0.90 for P. vivax; 0.54 for P. malariae, and 0.91 for infection with any Plasmodium species.Seven studies (43.8%) showed that ovalocytosis might protect against malaria infection38. Fowkes et al.16 found no association of SAO with P. falciparum prevalence (P = 0.29) or with mean parasite density (P = 0.66). Kimura et al.17 found no difference in the prevalence of SAO between patients with malaria and controls (P > 0.8) and no difference in the frequency of SAO between patients with P. falciparum and P. vivax malaria. Although Kimura et al.31 discovered a link between a higher rate of ovalocytes and a lower risk of malaria infection, they found that SAO did not protect against malaria infection. Lin et al.32 showed no significant associations between P. falciparum infection and SAO (P > 0.2). O’Donnell et al.34 demonstrated no difference in the proportion of SAO in acute malaria and the community controls . However, the degree of ovalocytosis was significantly lower in children with SAO during acute malaria compared with the community controls (P = 0.025). O’Donnell et al.35 showed that the SAO genotype was not associated with the frequency of placental P. falciparum infection . Shimizu et al.37 reported no significant difference in the prevalence of asymptomatic malaria infection between participants with or without SAO (P > 0.05), and Stanisic et al.38 reported no significant difference in the frequency of malaria infection among participants with or without SAO (P = 0.62). Manning et al.33 reported a frequently higher proportion of ovalocytosis in P. falciparum (5.7%) than P. vivax (3.8%) and mixed infections (4.3%).Eight studies  showed no association of SAO with malaria infection or severity38 were included in the meta-analysis of the risk of malaria among participants with ovalocytosis. Overall, the results demonstrated no difference in the log OR of malaria between patients with and without ovalocytosis . The results of both tests indicated a publication bias among the included studies. Although the trim-and-fill method was applied to correct for publication bias, the subsequent results showed no difference in the log OR of malaria among patients with ovalocytosis compared with those without ovalocytosis .Publication bias was assessed using a funnel plot and Egger’s test. The funnel plot showed the asymmetrical distribution of the log OR and indicated the standard error of the log OR Fig. . Egger’s38. Meanwhile, studies by Rosanas-Urgell et al.15 and Foo et al.30 both showed lower odds of malaria infection in SAO. Conversely, only the study by Cattani et al.29 reported higher odds of malaria infection in SAO. According to Rosanas-Urgell et al.15, SAO provides partial protection against P. vivax but not against P. falciparum infection or uncomplicated disease. Nevertheless, our meta-analysis contradicted these results, with no difference in the risk of P. vivax infection between patients with and without ovalocytosis. These findings suggest that ovalocytosis may not be a protective factor against P. vivax infection, at least for the limited studies included in this meta-analysis.SAO is a disorder of the RBC membrane caused by a mutation in the gene for band 3 protein, which leads to the formation of unique linear oligomers in the RBC membrane. Our meta-analysis results demonstrated no association between SAO and malaria infection, and almost all of the included studies demonstrated no association between SAO and malaria infection15. Secondly, alterations in RBC structures may impair the binding of malarial proteins from the parasite to the RBC membrane, thereby inhibiting parasite growth within RBCs39. Thirdly, SAO RBCs may provide protection against cerebral malaria due to the redistribution of sequestered infected RBCs away from the brain32. Similar to the protection against cerebral malaria, SAO RBCs may prevent parasitised RBCs from adhering to receptors in the placenta, thereby protecting against placental malaria. However, a study on placental samples of pregnant women demonstrated that SAO RBCs did not reduce the incidence of placental malaria and, therefore, did not protect against placental malaria35. Another study on pregnant women revealed that primigravidae with SAO had a lower prevalence of placental infection than primigravidae without SAO, whereas there was no difference in the prevalence and severity of placental infections between SAO and non-SAO multigravidae, indicating that the gravidae status in women with SAO confounds the association between SAO and placental infection. Thus, the mechanism by which SAO RBCs protect against placental malaria among primigravidae was suggested to be non-immune- rather than immune-based28. Since SAO RBCs protect against cerebral malaria or severe diseases but not uncomplicated diseases, this suggests that the protection involves post-invasion mechanisms, such as preventing sequestration of infected RBCs41.The protective mechanism of SAO against malaria is unknown, and several theories have been proposed. Firstly, modification of the RBC membrane may affect the ability of merozoites to invade RBCs and prevent the development of parasites within RBCs38. However, some showed that ovalocytosis might protect against malaria infection30 or severe disease36. Nevertheless, our meta-analysis results demonstrated no association between SAO and malaria infection. These results were stratified according to the study design, without or with minimal heterogeneity in cohort studies38 and in cross-sectional studies37. High or substantial heterogeneity was found in three case–control studies29. Besides differences in study design, the controversial association of SAO in the literature was suggested to be caused by the presence of molecularly heterogeneous SAO because SAO without B3∆27 was associated with decreased malaria infection rates31. Additionally, another study indicated that the inconsistency of SAO-related protection against malaria might be due to the detection of SAO with low sensitivity34.Our systematic review showed heterogeneous associations between SAO and malaria in individual studies. Most studies demonstrated no association of SAO with malaria infections or severityP. vivax malaria, some studies have suggested that SAO mainly protects against P. vivax malaria. Thus, it would be interesting for further studies to refine the investigation to that of P. vivax exclusively to assess the extent of this protection without any interference from a concomitant P. falciparum infection.Our systematic review and meta-analysis had several limitations. Firstly, the number of studies included in the meta-analysis was limited, which might limit the interpretation of the results. Nonetheless, because the results of the studies included in the meta-analysis were consistent, the findings were sufficiently robust to support the conclusion that there is no link between SAO and malaria infection. Secondly, a meta-analysis of the odds of severe disease between severe and non-severe diseases could not be performed due to the limited number of studies reporting an association between SAO and malaria severity. Third, there was heterogeneity in the association between ovalocytosis and malaria infection between studies. Although we conducted a meta-regression analysis, no probable source of heterogeneity was found. Fourth, although our meta-analysis revealed that ovalocytosis did not protect against Our systematic review revealed the heterogeneity of individual studies regarding the association between SAO and malaria. However, our meta-analysis results demonstrated no protection of SAO RBCs against malaria infection. Hence, the association of SAO with malaria infection or disease severity should be further investigated by larger studies.Supplementary Information 1.Supplementary Information 2.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.


Health care-associated primary bloodstream infections (BSIs), defined as not secondary to an infection at another body site, including central line-associated BSI, are a leading cause of morbidity and mortality in patients in neonatal intensive care units (NICUs). Our objective was to identify factors associated with severe morbidity and mortality after these infections in neonates in NICUs.possible (one coagulase-negative staphylococci (CoNS)-growing blood culture) or proven . BSI consequences were collected prospectively as moderate morbidity  or severe morbidity/mortality .This ancillary study of the SEPREVEN trial included neonates hospitalized ≥2 days in one of 12 French NICUs and with ≥ 1 BSI during the 20-month study period. BSIs  were diagnosed in infants with symptoms suggestive of infection and classified prospectively as P < .01), fetal growth restriction (FGR) (P = .04), and proven pathogen-related BSI vs. CoNS-related BSI (P < .01). There were no differences in severe morbidity and mortality between proven and possible CoNS BSIs. In possible BSI, S. epidermidis was associated with a lower risk of severe morbidity than other CoNS (P < .01), notably S. capitis and S. haemolyticus.Of 557 BSIs identified in 494 patients, CoNS accounted for 378/557 (67.8%) and recognized bacterial or fungal pathogens for 179/557 (32.1%). Severe morbidity/mortality was reported in 148/557 (26.6%) BSIs. Independent factors associated with severe morbidity/mortality were corrected gestational age <28 weeks (CGA) at infection . Neonates have a high risk of adverse events during their hospitalization in intensive care units (NICUs). Health care-associated infections (HAIs) are the most frequent adverse event in care . ExtremeHealth care-associated primary bloodstream infections (BSIs), as defined by the US Centers for Disease Control and Prevention , are infIn low birth weight neonates, sepsis is associated with a higher risk of mortality and morbidity, such as adverse neurodevelopmental outcomes and bronchopulmonary dysplasia, more ventilator days, and extended lengths of stay , 12–14.staphylococci (CoNS) are responsible for 21% to 78% of HAIs, followed by Gram-negative bacilli (15%–33%), Staphylococcus aureus (8%–17%), Enterococcus spp. (3%–16%), and fungi (4%–12%)  . Nonethespp. 3%–1%, and fu(4%–12%) . Among p% . Nonet(4%–12%) , 24.The SEPREVEN trial  was a stThis was an ancillary study of the SEPREVEN trial , which tHealth-care associated primary BSIs were defined according to the Centers for Disease Control and Prevention 2015 definition as laboratory-confirmed BSIs that were not secondary to an infection at another body site . We inclstaphylococci ; and iii) BSIs with only one blood culture growing a common commensal organism with clinical symptoms or signs compatible with infection and at least 5 days of antibiotic treatment   BSIs with one blood culture growing a recognized bacterial or fungal pathogen (proven pathogen-related BSI); (ii) BSIs with clinical symptoms of infection and two or more blood cultures collected on separate occasions growing the same common commensal organism such as coagulase-negative ted BSI) . Becauseted BSI) .We included all health care associated primary BSIs in the study and we distinguished central line-associated BSI (CLABSI), according to the US Centers for Disease Control and Prevention 2015 definition: a laboratory-confirmed BSI with an eligible central line in place for more than two consecutive calendar days and on the date of event or the day before .bacilli and S. aureus, time to positivity of blood culture). Patient's overall characteristics were collected prospectively within the SEPREVEN trial data collection.For each BSI, a physician, using a specific questionnaire, prospectively collected the following data: the patient's clinical characteristics and bacteriological data , time to positivity of blood culture, CLABSI, and catheter removal (early/late).Data are presented as medians  or means ± standard deviations (SD) for quantitative (continuous) variables and as numbers (percentages) for categorical variables. To compare characteristics between groups, we used Student P-value < .20 in the univariate analysis were entered into the multivariate analysis. We then applied a backwards stepwise approach to retain factors significant at the P < .05 level until we obtained a final model for each outcome.Factors associated with persistent bacteremia after initiation of antibiotic therapy were sought among the following: gestational age at birth, birth weight, sex, postnatal age at first positive blood culture (in days), time to positivity of blood culture (< or ≥ 12 h and < or ≥ 24 h), early removal of central line (yes/no), type of BSI, and presence of central line on the date of persistent positive blood culture (yes/no). Factors associated with the outcome at a P-value of.05 was considered significant.The association between severe morbidity/mortality and the organism involved was studied for each type of BSI. All analyses were conducted with Stata software (v 16). A The SEPREVEN study was approved by the National Data Protection Authority (CNIL no 915263) and the relevant ethics committees . The trial registration number was NCT 02598609. Parents were informed of data collection for the SEPREVEN study and did not oppose the use of their newborn's data.Among 6,099 patients included in the SEPREVEN cohort, 572 BSIs were collected in 506 patients. After 15 BSIs were excluded, the final study population comprised 557 BSIs in 494 patients . Table 1Common commensal organisms accounted for 378/557 BSIs (67.8%) and were mainly CoNS: 154/557 proven BSIs (27.6%) and 224/557 possible BSIs (40.2%). Recognized bacterial or fungal pathogens accounted for 179/557 (32.1%) BSIs .Among possible commensal-related BSIs, ≥ 2 blood cultures were collected in only 32/224 (15.5%) cases.Overall, moderate morbidity was found in 409/557 (73.4%) cases of BSI and severe morbidity/mortality in 148/557 (26.6%). The severe consequence was death in 35/557 (6.3%) BSIs, a life-saving treatment in 100/557 (17.9%) BSIs, an extended length of stay in 10/557 (1.8%), and possible permanent damage in 3/557 (0.5%).P = .02), a corrected GA <28 weeks at infection (P = .03), a birth weight <1,000 g (P = .02), and a recognized pathogen isolated in blood cultures (P = .03) (P < .01), FGR (P = .04), and a proven pathogen-related BSI  (P = .04) and a proven pathogen-related BSI (P = .03) were significantly associated with mortality.Factors significantly associated with severe morbidity/mortality in the univariate analysis were a GA <28 weeks compared to 28–32 weeks (P = .03) . In the P < .01) . ConcernP = 0.23) and similar among the different bacterial or fungal pathogens (P = 0.64). A possible BSI with only one blood culture growing S. epidermidis was significantly associated with a lower risk of severe morbidity/mortality compared to the other CoNS (P < 0.01)  .P = 0.68) and among the different CoNS species in proven BSI (P = 0.97), respectively.When considering only mortality, there was no significant difference among the different pathogens (P < .01) and a lower risk of persistent bacteremia in possible CoNS-related BSI  compared to proven pathogen-related BSI. Early removal of the central line was not associated with a significant difference in the persistence of bacteremia .Among the 529 infections with available data about blood cultures after initiation of antibiotic treatment, bacteremia persisted in 222/529 (42.0%) cases. In multivariate analysis, after adjustment for corrected GA at infection and FGR, the only factor significantly associated with persistent bacteremia was the type of BSI: there was a higher risk of persistent bacteremia in proven CoNS-related BSI , followed by S. capitis (21.8%) and S. haemolyticus (14.2%), again consistent with previous reports  regardless of the organism involved, and 168/224 (81.2%) in possible commensal-related BSI. If two blood samples had been collected for culture before antibiotic administration in all the cases, it would clearly have been easier to distinguish between real BSI and contamination.proven CoNS infection and those with possible CoNS infection suggests that many of the possible CoNS infections were true BSIs. Thus, other factors must be able to distinguish contaminants from true BSI. Time until the first culture is positive is likely to be the first clue in differentiating a contaminated sample from true BSI , it was more likely to be a true BSI than one positive for S. epidermidis.The lack of a significant difference in morbidity/mortality in our study between neonates with a true BSI . When onfections . We mighS. aureus, P. aeruginosa, or fungi), and when BSI continues despite 72 h of effective antimicrobial therapy (Another interesting finding in our cohort was that removing the catheter at an early stage of infection did not appear to protect patients from severe morbidity/mortality. Removing the central catheter in cases of infection is controversial –37. An i therapy . On the In our study, cultures remained positive after starting antibiotic treatment more often for proven CoNS-related BSI than for proven pathogen-related BSI. This could be due to the ability of CoNS to form biofilms on medical devices such as central lines, thus providing a permanent source and continuous seeding of bacteria into the bloodstream , 40. AnoWe had a higher proportion of persistent bacteremia in our cohort compared to other cohorts, which found persistence rates between 12% and 25% but it might be due to the large definition we took of persistent bacteremia –43.Our study has several limitations. First, despite the high number of infections, we might have lacked power to distinguish differences in outcomes according to the organism responsible. Second, connecting clinical consequences, such as prolonged length of stay to a BSI episode can be difficult, as numerous factors may influence this length of stay. Clinicians were asked to report consequences prospectively after the infection, but assigning consequences to infections and not to prematurity or other conditions can be difficult and could have resulted in imprecision in our data. Nonetheless, these consequences were reported by senior physicians with experience in neonatal intensive care. Third, we lacked detailed data on type and doses of antibiotics used, and the appropriateness of antibiotic therapy could have influenced clinical outcomes. Fourth, blood sampling practices could have varied among participating NICUs, and we know that the volume of blood and the number of cultures are important for identifying BSIs. However, before starting the inclusions in the SEPREVEN trial, blood sample practices before antibiotic treatment were standardized in the participating units, by informing and encouraging health care professionals to collect two separate blood samples for culture, of a volume ≥ 1 ml. Finally, the educational program conducted during the SEPREVEN trial might have modified the incidence of CLABSI. However, it seems unlikely that this intervention changed the microbiological distribution or outcomes related to BSI that we report here.S. epidermidis compared to other CoNS. Further studies to help distinguish real CoNS BSIs from contaminations are needed, along with data on pathogenicity according to the organism involved and patients' characteristics.Independent factors associated with severe morbidity/mortality related to the health care-associated primary BSIs were a corrected GA <28 weeks at infection, a recognized pathogen causing the BSI, and FGR at birth. There were no differences in severe morbidity and mortality between proven and possible CoNS BSIs. When only one blood culture was positive, severe morbidity/mortality were less frequent if it grew with


The gestational weight gain (GWG) and hyperglycemia are two key factors affecting adverse pregnancy outcomes among women with gestational diabetes mellitus (GDM). We aimed to investigate the combinatorial effect of abnormal glucose metabolism and GWG on adverse outcomes in GDM.This retrospective cohort study included 2611 pregnant women with GDM in Women’s Hospital School of Medicine Zhejiang University. Bases on the OGTT glucose levels, we categorized the GDM cohort into three subgroups: impaired fasting glucose (IFG) group, impaired glucose tolerance (IGT) group, and combined impaired glucose (IFG&IGT) group.Among pregnant women with IGT, insufficient GWG (IGWG) was an independent protective factor for pregnancy-induced hypertension syndrome (PIH) , macrosomia  and large for gestational age , as well as an independent risk factor for low birth weight infants  and small for gestational age ; and excessive GWG (EGWG) was related to increased risks of PIH , preterm delivery , postpartum hemorrhage , cesarean delivery  and low body weight infants . Moreover, EGWG was positively associated with PIH  in the IFG group. But there were no significant associations between either IGWG or EGWG and any pregnancy outcomes in women with combined IFG&IGT.The relationships between GWG and adverse outcomes were modified by abnormal glucose metabolism in women with GDM. Our results suggest that more specific GWG recommendations according to their metabolic state are needed for GDM. Gestational diabetes mellitus (GDM) is defined as an abnormality of glucose metabolism detected for the first time during pregnancy. The incidence of GDM increases gradually with the rise of obesity rate in women of childbearing age, endangering the health of mothers and infants , 2. The GWG is the reflection of normal pregnancy physiological functions . SeveralAs another important factor, the glucose levels tested by oral glucose tolerance test (OGTT) varied among pregnant women with GDM because of its specific diagnostic criteria , 14. In Actually, there might be a potential association between GWG and hyperglycemia during pregnancy. It is reported that EGWG can aggravate insulin resistance and further increase blood glucose during pregnancy in pregnant women with GDM . MeanwhiThis was a retrospective cohort study conducted in Women’s Hospital School of Medicine Zhejiang University between 1 July 2017 and 30 June 2018. There were 14638 pregnant women giving birth during study period, of which 2639 women with GDM were included in the study. Women with diabetes or hypertension history before pregnancy, multiple pregnancy, stillbirth, fetal anomalies, uncomplete medical records were excluded. We included 2611 women with GDM in the analysis.GDM was diagnosed according to the recommendations from International Association of Diabetes and Pregnancy Study Groups (IADPSG) using a 75-g OGTT . The criThe demographic and clinical data, including maternal age, gravidity, parity, height , pre-pregnancy weight , OGTT glucose levels, maternal weight at birth, gestational week at birth, mode of delivery, infant birthweight, pregnancy outcomes, were collected from medical records.2), normal weight (18.5 ≤ BMI < 25.0 kg/m2), overweight (25.0 ≤ BMI < 30.0 kg/m2), and obesity (BMI ≥ 30 kg/m2).Pre-pregnancy BMI was calculated by dividing pre-pregnancy weight in kilograms by height in meters squared. On the basis of World Health Organization (WHO) criteria, BMI was categorized in 4 groups: underweight  included gestational hypertension, preeclampsia or eclampsia, which were diagnosed by recommendations from International Society for the Study of Hypertension in Pregnancy . Pretermn = 234), IGT group , combined impaired (IFG&IGT) group .To determine whether different blood glucose abnormality in OGTT affected the relationship between GWG and pregnancy outcomes, we divided GDM cohort into three OGTT groups: IFG group  or number (percentage). The multiple comparisons of continuous variables were analyzed by ANOVA, and categorical variables were compared using χ2 analyses, as appropriate. Logistic regression analysis was applied to explore the association between GWG category  and pregnancy outcomes  in three OGTT groups. The odds ratio (OR) with 95% confidence interval (CI) were generally adjusted for maternal age, gravidity, parity, pre-pregnancy BMI, OGTT glucose levels  in multivariable analyses. Power analysis was conducted by Gpower 3.1 and showed sufficient power (å 80%) of our study to detect differences in the results. Statistical significance was considered as This study was approved by the hospital’s ethics committee (IRB-20200243-R), and informed consent was not required because of using anonymized patient records.2 and 12.4 ± 4.1 kg, respectively. Overall, 1417 (54.3%) pregnant women with GDM gained appropriate weight according to IOM weekly recommendations, whereas the rate of IGWG and EGWG were 578 (22.1%) and 616 (23.6%). However, the proportion of GWG categories was different according to guidelines for total weight gain during pregnancy regardless of gestational week. There were only 1152 (44.1%) and 462 (17.7%) women within and above the guidelines, while 997 (38.2%) women had insufficient weight gain during the pregnancy, which indicated that IGWG population will be overestimated if the effect of gestational week of delivery was ignored and therefore affected its relationship with adverse pregnancy outcomes. Furthermore, the average FBG, 1-h BG, and 2-h BG of total were 4.7 ± 0.6 (84.6 ± 10.8), 10.1 ± 1.4 (181.8 ± 25.2), and 8.9 ± 1.4 (160.2 ± 25.2) mmol/L (mg/dL), respectively. . Among the three OGTT groups, there were significant differences in maternal age, gravidity, pre-pregnancy BMI, GWG, gestational week at birth and prior cesarean delivery . Women with combined IFG&IGT had the highest maternal age but the lowest gestational week at birth, while women with IGT had the lowest BMI and women with IFG were youngest . In addition, women who gained insufficient weight were more likely to have IFG, while women with EGWG were more likely to have combined IFG and IGT in GDM.  women were categorized into the IFG group, 1970 (75.5%) into the IGT group and 407 (15.6%) into the IFG&IGT group. Of these, the IFG group and the IGT group had the lowest post-OGTT glucose and FBG respectively, while the highest OGTT glucose levels were found in the IFG&IGT group . Similar results were also found in neonatal outcomes . Interestingly, pregnant women with combined IFG and IGT had significantly increased incidences of PIH (15.7% vs 7.6%), preterm delivery (18.2% vs 11.3%), cesarean delivery (62.9% vs 52.1%), birth weight (3357.7 ± 608.3 g vs 3233.5 ± 515.6 g), macrosomia (13.0% vs 5.2%), LGA (32.7% vs 20.4%), only when compared to women with isolated IGT . However, as for postpartum hemorrhage, low body weight infants and SGA, there were not significantly different in the three OGTT groups.The relationship of OGTT glucose levels and pregnancy outcomes was displayed by Table P = 0.004) and SGA , and negatively associated with macrosomia  and LGA . EGWG was an independent risk factor for PIH , preterm delivery , cesarean delivery  and low body weight infants .Table P = 0.008) or SGA , while negative association with macrosomia  or LGA . Moreover, the adjusted OR of PIH was 0.55  for women with IGWG compared to those with AGWG in the IGT group. In contrast, pregnant women with IGT who gained excessive weight experienced significantly higher risk of PIH , preterm delivery , postpartum hemorrhage , cesarean delivery for nulliparous , and low body weight infants . Furthermore, we only found EGWG was an independent risk factor for PIH in the IFG group . There was no longer significantly association between GWG and any pregnancy outcomes in adjusted analyses of women with combined IFG&IGT, although EGWG seem to increase the risk of PIH and preterm delivery.  and PIH in women with IGWG ; while decreased the risk of low body weight infants in women with IGWG .In further stratified analysis of GWG, we also found combined IFG&IGT increased the risk of macrosomia in women with AGWG  and LGA . Similar reasons might exist for insignificant results in the IFG and IFG&IGT groups.GDM has been related to birth weight of newborns. Macrosomia and LGA were recognized as common complications in pregnant women with GDM , 31. HowActually, pathogenesis underlying IFG and IGT are different in general population. Although the insulin sensitivity in IFG and IGT are consistently decreased compared with normal glucose tolerance, their insulin resistance sites are different: IFG is mainly manifested as hepatic insulin resistance with poor suppression of hepatic glucose output, whereas its muscle insulin sensitivity is tend to be normal . The hepFurthermore, the interrelationship between hyperglycemia and obesity during pregnancy is also noteworthy . A studyIn response to the increasing incidence of GDM globally, effective prevention and treatment for GDM is a topical issue in women of childbearing age. “Precision medicine”, which means the ability to stratify patients by their susceptibility to a particular disease that can be treated in a better way, has become very popular in recent years . In factTo the best of our knowledge, this was the first study to explore the relationship between GWG and adverse pregnancy outcomes varied by OGTT glucose levels in pregnant women with GDM. Our findings suggest that different recommendations for weight gain during pregnancy need to be provided for GDM with different blood glucose impairment, consistent with the principle of precision medicine. Especially for pregnant women with IGT, proper weight gain during pregnancy is particularly important, while pregnant women with IFG and IFG&IGT should not ignore other potential risk factors  when paying attention to GWG. As such the personalized treatment plans can be provided for women with GDM to improve their perinatal outcomes. In addition, gestational week of delivery, a key determinant affecting the adequacy of GWG, was taken into account in our study. We used weekly GWG recommendations rather than total GWG recommendations of IOM to prevented misestimating the actual GWG classification, which might affect its relationship with adverse pregnancy outcomes.However, there were still several potential limitations in our study. First, this was a hospital-based retrospective study and the sample size was relatively small, especially for women with impaired fasting glucose, which might affect the power and generality of our study. Second, data on type of therapy and glycemic controls during pregnancy after diagnosis of GDM were lacking in this study, which might also affect the risk of adverse outcomes. Large prospective studies are needed in the future to further explore the association between GWG and perinatal outcome in GDM according to metabolic status, and to further explore the role of GDM treatment in it. Third, limited by the unclear pathogenesis of GDM, we cannot make a comprehensive explanation of the different perinatal outcomes caused by different abnormal glucose metabolism in pregnant women with GDM, which is also the direction of our future research. Another limitation was that pre-pregnancy weight was self-reported, therefore reporting bias could not be excluded. But high correlations were found between self-reported and measured weight before pregnancy , so it wIn summary, the heterogeneous relationships between GWG and adverse outcomes were found in GDM with different abnormal glucose metabolism. Our findings indicated that pregnant women with GDM might need more specific and strict recommendations to manage weight gain during pregnancy, especially in women with IGT. Stratified management and precision intervention for GDM will be a direction to improve the perinatal outcome of GDM and delaying the occurrence of long-term maternal and infant diseases.


This study proposes a “modular management” approach for vestibular neuritis (VN) to reduce chronicization and improve patient prognosis. The approach involves multi-factor grading and hierarchical intervention and was found to be more effective than traditional treatment strategies.This retrospective analysis compared two groups of VN patients from two medical institutions. The intervention group of 52 patients received “modular management,” while the control group of 51 patients did not receive this kind of management. Analyzed the early treatment strategies, 6-month prognosis, and other indicators of the two groups of patients, compared and analyzed their overall prognosis, and identified the risk factors affecting the chronicization.The modular management group had lower dizziness severity, better balance, lower anxiety, and higher video head impulse testing (v-HIT) gain after 6 months of onset. Analysis of factors related to persistent postural-perceptual dizziness (PPPD) in patients with VN showed positive correlations between the time from onset to diagnosis and PPPD, and Vertigo Symptom Scale (VSS), Dizziness Handicap Inventory (DHI), anxiety, and depression. Normalized vestibular rehabilitation was negatively correlated with PPPD, while gender, age, and early steroid use had no significant correlation. The multi-factor logistic regression model correctly classified 93.20% of the study subjects with a sensitivity of 87.50% and specificity of 94.90%.The proposed “modular management” scheme for VN is a comprehensive and dynamic approach that includes health education, assessment, rehabilitation, therapy, evaluation, and prevention. It can significantly improve patient prognosis and reduce chronicization by shifting from simple acute treatment to continuous management. Kim et al.  suggesteThis study proposes a modular management approach for VN, which shifts from simple acute-phase diagnosis and treatment to continuous, comprehensive, and dynamic management that includes disease knowledge dissemination, vestibular function assessment, vestibular rehabilitation (VR), cognitive behavioral therapy (CBT), prognosis evaluation, and prevention of chronicization. This study aims to investigate whether the “modular management” approach can improve the prognosis, reduce chronicization, and promote rapid recovery of social function in VN patients.2.2.1.2.1.1.2.1.1.1.Patients with acute onset vertigo within 14 days, or with spontaneous nystagmus (SN) to the healthy side on bedside examination.2.1.1.2.Perform a comprehensive physical examination of neurology and otology to differentiate and establish a definitive diagnosis. The evaluation should include the following components: ① Spontaneous nystagmus (SN), gaze-evoked nystagmus, and fixation suppression ; ② Bedside horizontal head impulse test and ocular tilt reaction (OTR); ③ Romberg test, tandem gait test, and finger-to-nose test; and ④ Tuning fork hearing test and otoscopy.2.1.1.3.1. The type and timing of vestibular function tests should be based on the patient’s tolerance. When a patient is in generally well health to tolerate vestibular function tests, these tests should be conducted as early as possible. Such tests are beneficial for accurate diagnosis, individualized vestibular rehabilitation (VR) program development, and prognosis evaluation.The required tests include: ① Video nystagmography and caloric testing; ② Video head impulse test (v-HIT).Optional tests include: ① Vestibular-evoked myogenic potential testing (VEMP); ② Fundus photography and ocular tilt reaction testing; and ③ Mid-frequency rotation.2. Complete cranial MRI and pure tone audiometry (PTA) testing to rule out central structural lesions or otologic disorders. If necessary, other imaging tests such as CT plain or enhanced of the inner ear.2.1.1.4.Symptomatic support: In the initial 48 h of VN onset, patients may experience severe dizziness, nausea, and vomiting. Symptomatic relief with vestibular suppressants is important to prevent complications, but prolonged use can hinder central compensation .Glucocorticoid: For patients without contraindications to steroid use, short-term low-dose corticosteroid therapy can be given during the acute phase. The specific regimen is as follows: prednisone 1 mg/kg/day for 5 consecutive days, followed by tapering to 0.5 mg/kg/day for 3 consecutive days.VRT: To facilitate clinical practice, we have divided the rehabilitation program for the acute phase of VN into two stages based on the characteristics of the disease course. Details below.2.1.1.4.1.Seated oculomotor training (for patients whose static compensation has not been fully completed and who still have SN). ① The exercise involves fixing gaze on a visual target  and maintaining eye position, followed by closing the eyes for 2–3 s and reopening them to continue gazing at the same target; ② Visual tracking exercises; and ③ Eye scanning exercises.Postural exercises (for patients whose static compensation has been mostly completed and whose SN has disappeared or significantly weakened). ① Standing eyes-open and eyes-closed training; ② Weight shift training; ③ Gait training; ④ Single-leg standing training; and ⑤ Tandem standing training.2.1.1.4.2.(For patients who have completed the acute phase training program) ① Standing, walking, and turning without visual cues or with altered proprioceptive feedback (such as foam pads or moving platforms) present; ② Gaze stability training  ; ③ The v4. Patients’ education: Dizziness specialists should educate patients and their families about the illness characteristics and prognosis from consultation to complete recovery. The communication should focus on the underlying reasons for dizziness, recovery principles, and benign prognosis. Various forms of communication, such as verbal, written, video, group counseling, and individual Q&A sessions, can be used.5. Promote VC drug: Recommend using drugs that promote vestibular compensation (VC), such as EGb761, an extract of Ginkgo biloba  and betahistine dihydrochloride   training, memory-based saccade training; ⑥ Turning exercise; ⑦ Obstacle avoidance training; ⑧ Complex background gaze training; and ⑨ Endurance training.Promote VC drug: The same as acute phase.Thoroughly evaluate anxiety and depression levels in patients. Use cognitive behavioral therapy (CBT) for mild cases and anti-anxiety/antidepressant treatments for moderate to severe cases to increase compliance with rehabilitation. The 14-item Hospital Anxiety and Depression Scale (HADS), Vertigo Symptom Scale (VSS), and Dizziness Handicap Inventory (DHI) are helpful tools for clinicians to objectively assess the physical and mental conditions of patients.2.2.5.Inclusion criteria for the modular management group were: ① patients with VN treated in vertigo specialty clinics at Shanghai Changzheng Hospital and Wuhan Union Hospital between January 2019 and June 2021. The diagnosis of acute VN was based on a history of sudden onset of vertigo, without auditory or neurological symptoms. The clinical findings comprised of spontaneous contralesional horizontal-torsional nystagmus that did not change direction with gaze and increased without visual fixation and an ipsilesional pathologic head impulse test , it alsoThe control group comprised patients diagnosed with VN at Shanghai Changzheng Hospital and Wuhan Union Hospital or in a vertigo specialty clinic within 1–6 months after onset, excluding those in the modular management program. Telephone interviews were conducted for follow-up assessments.This study was approved by the Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (NO. 20210873). All procedures performed in the studies involving human participants were in strict accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.2.3.2.3.1.The 14-item Hospital Anxiety and Depression Scale (HADS)  has fourVertigo Symptom Scale (VSS) . The scaDizziness Handicap Inventory (DHI)  which ha2.3.2.Patients in the modular management group have vestibular function tests 1 week after diagnosis and at 6 months after onset. Non-modular group (control group) patients have tests at 6 months after onset upon study entry. Tests are performed by technicians with at least 3 years of experience.2.3.2.1.Subject lies flat with head elevated by a 30° pillow. Eye movement recording system measures SPV, CP, and DP. Air irrigation at 24 and 50°C with 5 L/min for 60 s (2.3.2.2.Video head impulse testing conducted using videonystagmography system with subjects positioned 1.2 m from eye-level target. Goggles secured with elastic band. Multiple rotations performed on each side .2.3.3.Diagnostic criteria for PPPD: (1) Persistent dizziness/unsteadiness lasting ≥3 months. (2) Symptoms during upright position or triggering situations. (3) Not explained by other conditions. (4) Not accounted for by psychiatric disorders. (5) Causes distress/impairment. Diagnosis requires all criteria and excludes other conditions .2.4.s and compared using a t-test if meeting the assumptions; otherwise, the Mann–Whitney U test was used. Categorical data were presented as n and %, and compared using χ2 test or Fisher’s test. Kendall’s tau-b correlation and multivariate logistic regression analysis were performed to establish a regression model and evaluate multicollinearity. The ROC curve was used to analyze the predictive value of each indicator for PPPD development. The significance level was set at 0.05.In SPSS 23.0 software, normality and homogeneity of variance were tested before comparing continuous data. Normally distributed data were expressed as 3.In the modular management group, 57 patients were followed up, but two patients were uncooperative during examinations, one patient was intolerant to treatment, and two patients were lost to follow-up. Therefore, 52 patients were included in the study. In the control group, 56 patients were followed up, but four were uncooperative during examinations and one was lost to follow-up, resulting in 51 patients being included in the study. Overall, 103 patients were included in the study . Average3.1.p > 0.05) between the two groups in age, gender, time of visit, and various scale parameters during the early stage of the disease (Baseline comparison showed no significant differences ( disease .3.2.p < 0.000). At 6 months after disease onset, all enrolled patients underwent reassessment of vestibular function, with the following parameters recorded: canal paresis (CP) values  and affected-side horizontal video head impulse test (vHIT) gains . In the modular management group, the parameters for the two groups at 6 months were as follows: 32 patients (61.538%) achieved normalization of CP values, 34 patients (65.385%) achieved normalization of vHIT gains, and 23 patients (44.231%) achieved normalization of both parameters. In contrast, in the non-modular management group, the respective numbers were 25 patients (49.020%), eight patients (15.686%), and four patients (7.843%). At 6 months after onset, the modular management group had better recovery of vHIT gain and lighter symptoms of dizziness, balance disorders, and anxiety than the non-modular group . There was no significant correlation between gender, age, or early use of steroids and the development of PPPD  and accurately classified 93.200% of patients with a sensitivity of 87.50% and a specificity of 94.900%. There was no multicollinearity among the six predictor variables included in the model. The DHI score, anxiety level, and whether standard VRT was performed at 6 months after onset were identified as independent factors influencing the development of PPPD in patients with VN , followed by DHI score at 6 months  and whether the patient received standardized vestibular rehabilitation therapy . The ROC curves are shown in We plotted ROC curves to predict PPPD in patients using different indicators. The results showed that anxiety level at 6 months after onset had the highest area under the curve . All procedures performed in the studies involving human participants were in strict accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.SZ and JZ designed the research and directed its implication. FL, JX, and DL prepared and analyzed the data and reviewed drafts of the manuscript. JW, LL, RG, and XZ contributed to the manuscript’s modifications. All authors contributed to the article and approved the submitted version.This work was supported by grants from the National Natural Science Foundation of China (Nos. 82171152 and 81873701), the Fundamental Research Funds for the Central Universities (HUST: YCJJ202201040), and Knowledge Innovation Program of Wuhan-Basic Research (No. 2022020801010458).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.