The genetic material of the three domains of life  is always double-stranded DNA, and their GC content (molar content of guanine plus cytosine) varies between ≈ 13% and ≈ 75%. Nucleotide composition is the simplest way of characterizing genomes. Despite this simplicity, it has several implications. Indeed, it is the main factor that determines, among other features, dinucleotide frequencies, repeated short DNA sequences, and codon and amino acid usage. Which forces drive this strong variation is still a matter of controversy. For rather obvious reasons, most of the studies concerning this huge variation and its consequences, have been done in free-living organisms. However, no recent comprehensive study of all known viruses has been done . Viruses, by far the most abundant biological entities on Earth, are the causative agents of many diseases. An overview of these entities is important also because their genetic material is not always double-stranded DNA: indeed, certain viruses have as genetic material single-stranded DNA, double-stranded RNA, single-stranded RNA, and/or retro-transcribing. Therefore, one may wonder if what we have learned about the evolution of GC content and its implications in prokaryotes and eukaryotes also applies to viruses. In this contribution, we attempt to describe compositional properties of ∼ 10,000 viral species: base composition , correlations among non-coding regions and the three codon positions, and the relationship of the nucleotide frequencies and codon usage of viruses with the same feature of their hosts. This allowed us to determine how the base composition of phages strongly correlate with the value of their respective hosts, while eukaryotic viruses do not (with fungi and protists as exceptions). Finally, we discuss some of these results concerning codon usage: reinforcing previous results, we found that phages and hosts exhibit moderate to high correlations, while for eukaryotes and their viruses the correlations are weak or do not exist. Viruses are obligate parasites of all free cellular life forms and are, at the same time, the most abundant biological entities on Earth . To undePrevious phylogenetic studies carried out in different viruses have high-lighted mutational pressure as the major factor in shaping virus evolution in comparison with natural selection . NeverthIn prokaryotes and eukaryotes, the analyses of these features have led to several conclusions, and perhaps the more relevant for our current purpose can be summarized as follows: (i) base composition is generally more similar within phylogenetically close groups and species living in the same –or very similar– environment , (ii) foHowever, although some recent publications have analyzed several viruses  different viruses , show different properties at their base composition; (ii) there are strong compositional correlations among non-coding regions and the three codon positions; (iii) while GC content of phages strongly correlates with the genomic GC of their hosts, this is not the case for eukaryotic systems; and (iv) in general, the codon usage of phages is dependent of the codon usage of prokaryotes, while the codon usage of animal and plant viruses do not seem to be adapted to the codon usage of their hosts, with the probable exception of fungi and protists.ftp://ftp.ncbi.nlm.nih.gov/genomes/refseq/viral/ , were calculated for genomic regions extracted with BEDTools . Host GCv/genome . Codon uv/genome .https://www.genome.jp/virushostdb  coefficient was used to access the goodness of fit of linear regression models to the data. All these computations were implemented in R v4.0.[0-5]  was chosen to measure the strength of a linear association between variables. The adjusted R.0.[0-5] . Figures.0.[0-5]  using RC.0.[0-5] .p-value = < 0.0001) displaying two modes: a major at a GC of 43% and a minor at 62%. This distribution also presents three shoulders at ≈ 30%, 36%, and 49%, being the latter more evident than the others.In p-value ≈ 0), with three modes at 39%, 51% and 63%. While the value of 39% is representative of the whole sample . In The other group of retro-transcribing viruses, +ssRNA-RT, tends to present bimodal distributions in all four bases , as is tcis-acting elements. Despite this, non-coding and genomic GC display a very high correlation (ρ = 0.86).As happens in prokaryotes and most parasitic or symbiotic unicellular eukaryotes, for viruses protein-coding regions make up the majority of their genomes. In summary, only 9% (median) of a viral genome is not transcribed and translated. However, these regions are usually highly structured and encode p-values ≈ 0). The Spearman correlation coefficients between non-coding GC and GC1, GC2, and GC3 are 0.76, 0.77, and 0.77, respectively. Also, they present big differences in the slopes: 0.57 (GC1), 0.41 (GC2), and 1.37 (GC3). The correlations that hold between non-coding regions and GC1, GC2, and GC3 in viruses sorted by Baltimore classification are displayed in In n = 3,697 host-phage pairs). This holds when considering separately Bacteria  or Archaea . It is interesting to note that most phages display lower GC values than their hosts. This is noticeable in Besides these compositional correlations, inherent to each viral genome, it is of great interest to search for putative dependencies with respect to their hosts. This is displayed in n = 4,642 host-virus pairs). This figure represents the relationship of eukaryotes and their viruses, colored by eukaryotic subgroup . No meaningful correlation exist between viruses and animals  or plants and their viruses . Conversely, fungi and mycoviruses , do present a moderate positive correlation . Protists and their viruses exhibit a negative correlation , which, although moderate, is a polarizing result.Contrary to what is provided for prokaryotes, eukaryotic viruses show a very weak correlation between their GC values and that of their hosts; see 2 also captures these strong differences between phages and eukaryotic viruses: 0.52, and less than 0.01, respectively.Given the pattern described above regarding GC content, we further analyzed the relationship between codon usage of viruses in relation to that of their hosts. In Escherichia and Mycobacterium genera, respectively. Despite the previous points, we hypothesize that the maximum of the distribution (GC content peaking at 43%) will not change significantly, as will not the minimum and maximum values. We postulate this latter point, given the nature of the genetic code and the correlations that hold between the global GC content and GC1, GC2, and GC3 (see below). Indeed, these two factors impose constraints on codon usage and on the frequencies of the amino acids that can be coded by each virus , shown in ch virus .In this study, we have shown that when sorting viruses according to Baltimore classification, several differences among them are apparent. A singular behavior is seen in the case of dsDNA viruses. While unimodal distributions are found in dsRNA and dsDNA-RT , a trimoThe bimodal distributions of bases from +ssRNA-RT  are intrWe expected that single-stranded  viruses should display, on average, remarkably lower G and C frequencies in relation to double-stranded, since ss genomes are prone to mutations toward A and T/U . HoweverRegarding compositional correlations, the main conclusions that can be reached  are the (b) In spite of the previous point, as happens with prokaryotes and eukaryotes , the strIn summary: (i) these correlations, that hold between non-coding and coding regions and their codon positions are indeed universal. (ii) They are independent of the genetic material: indeed, they can be seen not only in prokaryotes and eukaryotes  but in viruses, which as known, can be ss or dsRNA, ss or dsDNA, retrotranscribed or not. They are independent of the (iii) host and of (iv) the replication enzymes. (v) The structure of the genetic code is the main force that imposes limits to the “degree of freedom” of the correlations with the three codon positions. Hypothetically, a steeper slope between the non-coding sequences of viruses with GC2, similar in magnitude to the one found for GC3 (1.37), could cause that some amino acids would not be used (or used at extremely low frequencies) in viruses displaying extremely high (or low) GC content.The study of GC content of viruses in relation to the GC content of their hosts (eukaryotes and prokaryotes) displays two completely different patterns. While in the majority of eukaryotes  there appears to be no relation , in prokConcerning codon usage, we found a similar pattern as in genomic compositional correlations , correlations among non-coding regions and the three codon positions, and the relationship of viral genomic base composition and codon usage with the same feature of their hosts. This allowed us to confirm, with a high number of viruses and hosts, that the genomic base composition and codon usage of phages strongly correlates with the respective values of their hosts. In contrast, as previously but not consensually reported, animal and plant viruses show no correlation between their GC content and that of their hosts. Finally, while all 64 codons show positive correlations between phages and hosts values, in contrast, for eukaryotes and their viruses, overall, the correlations are weak or do not exist.https://github.com/lompa/virushostgc.The source code and datasets presented here are available on GitHub at: DS and HM conceived and designed the work and drafted the manuscript. DS conducted all bioinformatics analyses and arranged figures and tables. DS, JC, and HM revised the manuscript, participated in the literature search and discussion, and read and approved the final manuscript. All authors contributed to the article and approved the submitted version.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.


Glucose variability (GV) is a common complication of dysglycemia in critically ill patients. However, there are few studies on the role of GV in the prognosis of pediatric patients, and there is no consensus on the appropriate method for GV measurement. The objective of this study was to determine the “optimal” index of GV in non-diabetic critically ill children in a prospective multicenter cohort observational study. Also, we aimed to confirm the potential association between GV and unfavorable outcomes and whether this association persists after controlling for hypoglycemia or hyperglycemia.Blood glucose values were recorded for the first 72 h and were used to calculate the GV for each participant. Four different metrics  were considered and compared to identify the “best” GV index associated with poor prognosis in non-diabetic critically ill children. Among the four metrics, the SD was most commonly used in previous studies, while GLI- and MAG-integrated temporal information, that is the rate and magnitude of change and the time interval between glucose measurements. The fourth metric, the average consecutive ACACP, was introduced in our study, which can be used in real-time clinical decisions. The primary outcome of this study was the 28-day mortality. The receiver operating characteristic (ROC) curve analysis was conducted to compare the predictive power of different metrics of GV for the primary outcome. The GV index with the largest area under ROC curve (AUC) was chosen for subsequent multivariate analyses. Multivariate Cox regression analysis was performed to identify the potential predictors of the outcome. To compare the contribution in 28-day mortality prognosis between glycemic variability and hyper- or hypoglycemia, performance metrics were calculated, which included AUC, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).n = 97) died within 28 days after admission to the pediatric intensive care unit (PICU). Statistically significant differences were found between survivors and non-survivors in terms of four GV metrics , in which MAG  achieved the largest AUC and showed a strong independent association with ICU mortality. Subsequent addition of MAG to the multivariate Cox model for hyperglycemia resulted in further quantitative evolution of the model statistics . The impact of hyperglycemia  on outcome was attenuated and no longer statistically relevant after adjustment for MAG .Among 780 participants, 12.4% (GV is strongly associated with poor prognosis independent of mean glucose level, demonstrating more predictive power compared with hypoglycemia and hyperglycemia after adjusting for confounding factors. GV metrics that contain information, such as time and rate of change, are the focus of future research; thus, the MAG may be a good choice. The findings of this study emphasize the crucial role of GVs in children in the PICU. Clinicians should pay more attention to GV for clinical glucose management. Glucose management is an important element of intensive care management. In general, glucose dysregulation is related to negative outcomes in critically ill patients and is a multi-system complication . A few aIn 2006, Egi et al.  demonstrThe GV, also known as glucose fluctuation, represents the fluctuation of glucose levels over time and refers to a non-stationary state in which glucose levels fluctuate between high and low values, such as speed, magnitude, and frequency. A few GV metrics appear to be correlated with worse outcomes in critically ill patients, such as standard deviation , 8, 13, Our retrospective study  also demThis was a prospective, multicenter, observational study conducted between January 1, 2020 and December 31, 2020. The participants were recruited from five PICUs that belong to tertiary A hospitals in southwest China and represent the top intensive care centers, providing top-tier medical care to most critically ill children in these regions. The ethical committee of the Children's Hospital of Chongqing Medical University approved this study , and the clinical trial registration was completed (ChiCTR2000030846).Based on the literature, a sample size of 665 can produce 90% power at the 5% level of significance when considering a 20% lost-to-review rate, which was calculated based on the primary outcome-28-day mortality in PICU with cohort study analysis using an estimated intensive care unit (ICU) mortality of 10% and a risk ratio of 2 , 22.Before initiating the study, we provided professional training for data loggers in each research PICU, including project objectives, research progress, and data collection. Multi-center data collection was performed by recorders under strict guidelines. After data collection and entry, the data were sent to a central repository where they were corrected for any suspicious errors or missing values by the chief coordinator and physicians.All critically ill children aged 36 weeks  to 16 years who were receiving vasoactive drug support for hypotension or ventilatory support for respiratory failure was considered for inclusion. Exclusion criteria included the following children: (1) those who remained in the PICU for <24 h, (2) those who were diagnosed with diabetes mellitus or inborn errors of metabolism, (3) those who had utilized glucocorticoids or total parenteral nutrition (TPN) treatment for 3 days after PICU admission; (4) those who were readmitted to the PICU.Data were manually entered into the Epidata software, version 3.1. For each patient, demographic data , clinical treatments , and prognosis , and length of time in the PICU) were recorded. The primary outcome was 28-day mortality. Secondary outcomes were the number of days alive and free from MV at 28 days  after admission. If the patient died within 28 days, we recorded a 0. If a patient survived for a period within 28 days, we recorded 28 minus the survival time; the smaller this value, the worse the prognosis, which is similar to the record of MV free days to day 28. In addition, the incidence of MODS was recorded as a poor prognosis indicator. The follow-up for each patient admitted to our PICUs was performed and recorded by the nurse 1 month after discharge using telephone interviews ]/d, (mmol/L)2/h/d) 2/h/d) , and meaL)2/h/d) . In addiL)2/h/d)  was intrL)2/h/d) , 25.As per the existing unit policy, dextrose infusion is stopped when the glucose value exceeds 15 mmol/L and actively deals with the primary disease . Then, wn (%), and continuous variables without normal distribution were described by the median (IQR). For comparisons between the two groups, the Wilcoxon rank-sum test was used for continuous variables without normal distribution, while the chi-square test was used for categorical variables. We used ROC curve analysis to compare the predictive power of different indicators of GV for the primary outcome. The GV index with the largest area under ROC curve (AUC) was also converted to a categorical variable based on the cut-off value for subsequent analysis. The GV index and hypo/hyperglycemia were gradually substituted into a multivariate Cox regression model adjusted for confounders with a significance level of p ≤ 0.10 to prevent the exclusion of significant variables from the regression model. The results are expressed as hazard ratio (HR) (95% CI). The overall predictive accuracy of the model was quantified using the AUC. In addition, net reclassification improvement (NRI) and integrated discrimination improvement (IDI) analyses  of the 780 participants died within the 28-day period after PICU admission. At the primary endpoint , participants were divided into survival and non-survival groups. The primary reasons for ICU admission included 353 cases of respiratory diseases (such as severe pneumonia and asthma), 35 cases of cardiovascular diseases (such asshock), 130 cases of neurological diseases, 85 cases of trauma, 14 cases of postoperative care, 58 cases of sepsis, and 105 other cases . A total of 9,142 glucose values were collected of which 65.9% were from arterial blood gas analysis, 3.4% from venous blood, and 30.7% from capillary blood, with a median frequency of 11 (IQR: 8.25–14) glucose monitoring per patient. Demographic information, clinical features, and disease severity between the survival and non-survival groups are shown in p < 0.05).AUC > 0.700, p < 0.05). However, MAG  has the largest areas compared to other GV indices in the prediction of PICU 28-day mortality. MAG had significantly better discrimination of PICU 28-day mortality than the other metrics (p < 0.05). Therefore, we used MAG to represent GV and explore its impact on adverse outcomes. Additionally, we utilized an optimal cut-off value for 28-day mortality prediction.The AUCs are shown in p ≤ 0.1 in HR: 2.795, 95% CI: 1.687–4.632, p < 0.001) was still related to PICU mortality after adjustment for hypoglycemia . There was no interaction between MAG and hypoglycemia.We sequentially included the baseline variables in the Cox regression model with glycemia , Model 3HR for hyperglycemia, 2.026; 95% CI: 1.240–3.310; p = 0.005) . After adjusting for MAG , the effect of hyperglycemia   , Model 2= 0.216) , Model 36) (2-df : 9.115 ap = 0.016). We used Cochran's and Mantel–Haenszel statistics to further explore the relationship between hyperglycemia, MAG, and mortality (p < 0.001), regardless of the presence of hyperglycemia. The test of homogeneity of the odds ratio (OR) was p = 0.022; therefore, after stratifying by MAG, in the low GV group, hyperglycemia was relevant to PICU mortality . In the high GV group, hyperglycemia did not affect mortality . This means that patients with hyperglycemia will have different mortality rates depending on the MAG. In contrast, in the high GV group, hyperglycemia did not play a role in mortality events. These results support the possibility that GV may be a stronger predictor of 28-day mortality than hyperglycemia.However, the interaction between hyperglycemia and MAG was statistically significant . Similar results were observed in the ventilator-free days . The incidence of MODs was significantly higher in the high-GV group than in the low-GV group .To our knowledge, this is the first multicenter prospective study of pediatric dysglycemia  conducted in Southwest China, which attempted to establish an association between dysglycemia and 28-day mortality in PICUs. Several important relationships were also observed. First, GV was independently associated with unfavorable outcomes, and it may play a more predictive role in PICU mortality than hyperglycemia and hypoglycemia. These findings emphasize the important role of GV in children admitted to the PICU. Also, MAG may be a dominant index reflecting glucose fluctuations, which considers the speed and magnitude of change, and the time series among glucose values.A strong relationship between increasing GV and poor prognosis has been consistently reported in adult patients –10, 31. OR of ICU  and hospital  mortality, compared to only hypoglycemia or hyperglycemia, or neither. Several in vitro or animal experiments . Among all four indices, MAG obtained the largest AUC and was found to be a better indicator of GV than SD. Hermanides et al. . Based on the results of previous studies . ROC analysis showed that three of the four GV indicators  had a moderate predicted value for 28-day PICU mortality , and minor(s)' legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.JL participated in the study design, project management, and supervision. MD and WL were responsible for the conceptualization and design of forms, data management and quality control, writing, and editing. JL, BH, YZ, GZ, JJ, and HF were responsible for the project implementation organization and management and data quality control. MD and YL were responsible for the statistical analysis and documentation. MD, WL, FL, JC, LD, DX, and KY were responsible for the recruitment of participants and documentation of data. All authors were contributed to this article and approved the submitted version.This study was supported by the General Project Fund for National Clinical Research Center for Child Health and Disorders (grant number: NCRC-2019-GP-12).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.


Objectives: As a common otology emergency, sudden sensorineural hearing loss (SSNHL) has a great impact on quality of life. The diagnosis and treatment of SSNHL remain challenging. This study aims to identify and investigate the association of liver functions with SSNHL.Methods: A total of 135 SSNHL patients and 135 sex- and age-matched controls were prospectively enrolled. The baseline and clinical characteristics, along with liver function levels of participators, were collected. Linear correlation, logistic regression, and receiving operator characteristic curve analysis tests were applied to examine the association between liver function levels and SSNHL.Results: There were no differences in baseline characteristics between SSNHL and control groups. The albumin (ALB) level of the SSNHL group was significantly lower than that in the control group (p < 0.001). The logistic regression showed that the low ALB level may be a predictive factor of SSNHL with an adjusted OR of 0.809 . By comparing the indicators of different prognosis groups, we found that the effective group had a significantly lower hearing impair onset and higher ALB (p = 0.001) and AGR (p = 0.003) levels than the ineffective group. Logistic regression revealed that the hearing level onset  and ALB level  showed strong associations with treatment outcome.Conclusions: Lower ALB levels, within the normal ranges, were associated with the occurrence and unfavorable outcome of SSNHL. However, further research on the underlying mechanisms needs to be conducted. Sudden sensorineural hearing loss (SSNHL) has been defined as sudden hearing impairment at least 30 dB over at least three consecutive frequencies on audiogram within 3 days and with an uncertain cause . The yeaThe liver function test is an important index to evaluate the pathological changes of liver function, which is widely used in clinical diagnosis. It can reflect the metabolism, immunity, and damage repair ability of the body. The main indicators include albumin (ALB), globulin (GLB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total bilirubin (TBIL). The physiological functions of ALB include maintaining osmotic pressure and transporting a variety of substances . The ALBGiven that liver function tests which include ALB are useful in assessing metabolism level, immune level, and damage repair level, observing the clinical data of SSNHL patients, we found that the liver function indexes, although within normal levels, had a certain trend in alteration. This may be related to the abnormal metabolism of the liver in the body and its influence on blood circulation. Therefore, the purpose of this prospective cohort study was to clarify the relationship between liver functions with SSNHL and to provide a theoretical basis for the prevention and treatment of SSNHL.A total of 135 consecutive patients with SSNHL diagnosed at our Hospital between July 2018 and December 2020 were prospectively enrolled. All participants provided written informed consent for their inclusion in the database and the use of their data for research purposes. The study protocol was approved by the Institutional Ethics Committee of the Shanghai Jiao Tong University Affiliated Sixth People's Hospital [2018-KY-036(K)]. This prospective cohort study was conducted in conformity with the Helsinki Declaration. Patients included in the study visited the hospital for the first time within 7 days after the onset of SSNHL. Based on the potential relationships between vascular factors and all frequencies SSNHL, we included patients with hearing loss at all frequencies and mean pure tone audiometry across 0.25 to 8 kHz ≥ 30 dBHL according to the American guideline of SSNHL , and blood pressure on admission (systolic blood pressure and diastolic blood pressure)] and clinical characteristics , was obtained from all patients. All hearing assessments were performed in standard shielding rooms, and pure tone average (PTA) was performed for both air and bone conduction at 0.125, 0.25, 0.5, 1, 2, 4, and 8 kHz before and after 2 weeks of systemic treatment. The hearing level of each patient was calculated by averaging the PTA of impaired frequencies after onset while the extent of hearing recovery is calculated using PTA after onset minus PTA after treatment. Temporal bone computed tomography or inner ear magnetic resonance imaging was performed on all patients to rule out ear structural abnormality and tumors. Upon admission, liver function tests of ALB, GLB, ALT, AST, and TBIL levels were performed using blood samples obtained from the antecubital veins of all patients between 6 and 7 a.m. after an overnight fast. The liver function tests were assayed using an automatic biochemical analyzer . The value of the albumin-to-globulin ratio (AGR) was obtained by the value of ALB divided by AGB.The patients underwent comprehensive treatment, including treatment with steroids and batroxobin, according to the Chinese guideline of SSHNL . All patp > 0.05); thus, the comparisons of age were performed using the independent-sample t-test. The data of height, weight, BMI, body pressure, time to treatment, hearing level onset, and liver function levels did not have a normal distribution (p < 0.05); thus, Kruskal–Wallis test was used for the comparison of median values of the groups. The chi-squared test was used for categorical variables. A linear correlation was performed to assess the association between liver function levels, hearing loss, and hearing recovery. The backward conditional logistic regression  was used to estimate the odds ratios (OR) and 95% confidence intervals (CI) for the correlation between liver function levels and the occurrence and outcome of SSNHL. The collinearity of all continuous variables was examined before performing the logistic regression using the variance inflation factor. To detect the most significant parameter related to the outcome of SSNHL, and to determine a cutoff value, the receiver operating characteristic (ROC) curve analysis test was used. p < 0.01 was considered significant for all tests to account for small sample size. The figures were generated using GraphPad Prism 7.0 for Windows .All statistical analyses were performed using SPSS for Windows version 24.0 . First, the relationship assessment of liver functions with the occurrence of SSNHL was performed. Then, the relationship of liver functions with the occurrence of SSNHL was statistically investigated. Descriptive variables are expressed as mean ± standard deviation, median (interquartile range), or a percentage, as appropriate. Data were investigated using the Kolmogorov–Smirnov test to determine the distribution pattern. The data of age had a normal distribution  were male, and the mean age of the patients was 49.07 ± 16.44 years. There were no differences in age, sex distribution, height, weight, BMI, and blood pressure on admission between the two groups. Compared with the control group, the SSNHL had a significantly lower ALB level (p < 0.001). The levels of GLB, ALT, AST, and TBIL were not significantly different between the two groups .A total of 135 SSNHL patients and 135 controls were enrolled. The baseline characteristics and liver function levels of participants are shown in p < 0.001).To investigate the relationship between liver function levels and the occurrence of SSNHL, we performed a linear correlation analysis and drew the scatterplots of liver function levels vs. severity of hearing loss, as shown in n = 51) and the ineffective group (n = 84) according to different levels of hearing recovery, as shown in p > 0.01). The clinical characteristics of the two groups had no significant difference , with the exception of hearing level being significantly higher in the ineffective group than that of the effective group (p = 0.007). Comparing the liver function levels of these two groups, we found that the effective group had significantly higher ALB (p = 0.001) and AGR (p = 0.003) levels than the ineffective group. We then performed a linear correlation analysis and drew the scatterplots of liver function levels vs. the recovery of hearing level, as shown in p = 0.062 and p = 0.063, respectively). p = 0.012) and 1.191 , the hearing level onset and ALB level showed a strong association with the treatment outcome. After adjusting for all other significant outcome predictors, the hearing level onset and ALB level remained independent outcome predictors with adjusted ORs of 0.976  and 1.181 , respectively. These results showed that worse hearing level onset and lower ALB level correlate with worse SSNHL functional outcome.The SSNHL patients were divided into the effective group  and interact with free fatty acids that inhibit their promoting effects on vascular tension and inhibit platelet aggregation and thrombosis , 34. Thel matrix . Endothel matrix . Finallyl matrix . HearingGLB is composed of a number of inflammation-related proteins . Thus, the increase in GLB reflects an inflammatory state . Since bin vivo and in vitro , the First Grant (2020YFC2005201) of the Chinese National Key Research and Development Program (2020YFC2005200), and the Shanghai Municipal Commission of Science and Technology (Grant No. 18DZ2260200).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.


Background and Purpose: To investigate the association of monocyte to high-density lipoprotein ratio (MHR) with disease severity and prognosis in patients with neuromyelitis optica spectrum disorders (NMOSD).Methods: This retrospective study included 125 patients with NMOSD. Demographic and clinical parameters, including the MHR, were assessed. The initial Expanded Disability Status Scale (EDSS) score and relapse rate were used to evaluate disease severity and prognosis, respectively. Correlations between MHR and disease severity and relapse rate were analyzed. The predictive value of MHR for prognosis was evaluated using receiver operating characteristic (ROC) curve analysis.Results: Compared with the low MHR group, the initial EDSS score  and relapse rate  were significantly higher in the high MHR group. MHR was positively correlated with the initial EDSS score . Multivariate analysis showed that MHR was significantly associated with severity , and it was a significant predictor of disease prognosis . The median relapse interval of the high MHR group was 24.40 months. When the MHR was higher than 0.565, the risk of relapse was high .Conclusion: MHR is a novel predictive marker of disease severity and prognosis in patients with NMOSD. Early monitoring and reduction of MHR may allow earlier intervention and improved prognosis. Neuromyelitis optica is an autoimmune demyelinating disease of the central nervous system that is characterized by acute optic neuritis and transverse myelitis occurring simultaneously or continuously –3, with As an important effector cell of the innate immune response, monocytes play a key role in the pathogenesis of autoimmune-related central nervous system diseases, including NMOSD –13. StudThe monocyte to high-density lipoprotein ratio (MHR) is a novel marker that reflects the degree of inflammation and oxidative stress. Many studies have shown that the MHR is closely related to the occurrence, development, and prognosis of cardiovascular, cerebrovascular –25, immuThis study was approved by the Ethics Committee of Zhengzhou University (2019-KY-018). All patients provided written informed consent to participate in this study. In this retrospective study, we collected the clinical data of 650 patients newly diagnosed with NMOSD from September 2013 to June 2020 at the First Affiliated Hospital of Zhengzhou University. The inclusion criteria were: (1) diagnosed with NMOSD for the first time according to the 2006 Wingerchuk standard or the 2015 McDonald NMOSD international general diagnostic standard , 33; (2)Clinical data were obtained through a retrospective review of the hospital electronic case system. The following clinical information was collected: gender, age, clinical phenotype at onset, comorbidities, blood cell count, triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL), HDL, MHR, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), anti-AQP4 antibody status, spinal magnetic resonance imaging (MRI), and treatment plan.Blood samples were collected from the antecubital vein at 8:00 am after an overnight fast and stored on ice before testing (within 30 min after collection). Blood samples were analyzed in the biochemistry laboratory of the hospital. Blood cell count and all biochemical parameters were determined by standard methods, and the enzyme kit  was used to evaluate fasting TC, TG, LDL, and HDL levels. The anti-AQP4 antibody status in serum or cerebrospinal fluid samples was analyzed by the cell-based assay method. MRI of the spinal cord was performed using a 3T MAGNETOM Skyra scanner  in the Department of Magnetic Resonance of the hospital at admission. Longitudinally extensive transverse myelitis (LETM) was considered as extending 3 or more vertebral segments on T2-weighted imaging . When moTo calculate the extended disability status scale (EDSS) score at the time of admission, at least two professional neurologists carefully reviewed the patient's clinical records. This was recorded as the initial EDSS score, which was used to evaluate disease severity. The baseline EDSS scores of all patients before the first attack were considered normal. Patients in the cohort were divided into those with mild disability (EDSS score 0–3.5) or moderate/severe disability (EDSS score 4–9.5) –36.The main index for evaluating prognosis was the relapse rate, which was defined as new or relapsed neurological symptoms caused by demyelinating diseases of the central nervous system without fever or infection, lasting at least 24 h, and increasing the existing EDSS score of patients by 0.5 points . Follow-t-test was used for comparison between the two groups. Data with non-normal distribution were expressed as median and interquartile range, and the Mann-Whitney U test was used for comparison between the two groups. The correlation between MHR and other clinical indices and the initial EDSS score was obtained by Spearman correlation analysis. Patients in the cohort were divided into those with mild disability (EDSS score 0–3.5) or moderate/severe disability (EDSS score 4–9.5)  and 95% confidence interval (95% CI). The Kaplan-Meier curve was used to analyze whether different MHRs had an independent influence on the time of the first relapse, and univariate Cox survival analysis was used to screen the variables with p < 0.2. Then the multivariate regression model was used to analyze whether MHR was a predictor of relapse in patients with NMOSD. The results were expressed as the risk ratio with the 95% CI. We used a receiver operating characteristic (ROC) curve to analyze the predictive value of MHR for disease prognosis, determine its optimal critical value, and calculate the area under the curve to evaluate the accuracy of the cutoff value. Differences were considered statistically significant at p < 0.05.Statistical analysis was performed using SPSS version 26.0 . According to the median MHR, the cohort was divided into two groups, and the classification data were expressed as percentages (%). The chi-square test or Fisher's exact test was used to compare the two groups. The Kolmogorov-Smirnov test was used for normality testing, and the measurement data conforming to normal distribution were expressed as mean and standard deviation; the independent sample e 4–9.5) –36. We uP = 0.005), and there were significantly more patients with LETM on MRI of the spinal cord . Other clinical parameters, such as lymphocyte count, TG, LDL, ESR, and CRP, were not significantly different between the two groups. There was a significant difference in the initial EDSS score between the two groups . The relapse rate of the high MHR group was higher than that of the low MHR group . Other demographic and clinical characteristics are shown in A total of 125 patients with NMOSD were enrolled in this cohort study . The aver = 0.210, P = 0.019), HDL , and CRP  were significantly correlated with the initial EDSS score of patients with NMOSD, but the correlation was weak (P > 0.05)   . In addi= 0.001) .P = 0.024). HDL levels  and MRI of the spinal cord showing LETM  had a moderate influence on the initial EDSS score. However, age, gender, hypertension, anti-AQP4 antibody status, blood monocytes, lymphocyte count, TC, TG, LDL, ESR, CRP, and lymphocyte to HDL ratio were not significantly correlated with the initial EDSS score. In the multivariate model, MHR was an independent risk factor for disease severity   .Kaplan-Meier analysis was used to evaluate the correlation between MHR and the relapse rate of patients with NMOSD. MHR was a significant predictor of disease prognosis (log-rank P=0.046). The median relapse interval of the high MHR group was 24.40 months, whereas 50% of patients in the high MHR group relapsed 28.60 months after treatment .p = 0.020) was significantly correlated with relapse risk. However, there was no correlation between age, gender, hypertension, anti-AQP4 antibody, blood lymphocyte count, CRP, lymphocyte-to-HDL ratio, initial EDSS score, treatment plan, and relapse rate (P > 0.05). Blood mononuclear cell count, TC, TG, HDL, LDL, ESR, and MRI of the spinal cord showing LTEM had a moderate influence on disease relapse (P = 0.046)  .P = 0.007), the best cutoff value was MHR = 0.565, the sensitivity was 0.333, and the specificity was 0.919, showing a good predictive ability for disease relapse. When the MHR is higher than 0.565, the risk of relapse is high , and patients with high MHR had a higher relapse rate, which was an independent risk factor for disease severity and poor prognosis. Recently, it has been reported that the MHR of patients with multiple sclerosis with EDSS score ≥4 is significantly higher than that of patients with EDSS score <4, which proves that MHR is related to the severity and disability of MS and can be used as an independent indicator to predict disability   . The ser= 0.008) . CRP lev= 0.008) , 61.Our study also has several limitations worth noting. First, the retrospective study design has inherent defects. Second, this was a single-center cohort study with a relatively short follow-up time; therefore, a larger multi-center study with a longer follow-up period is warranted to confirm our results. Third, our hospital did not evaluate MOG antibody levels prior to 2019, and the lack of data prior to this date may have affected the research results. Fourth, selection bias should be considered. Because of incomplete test data, loss to follow-up, and other reasons, we only studied a small number of patients (125/650). Finally, some uncontrollable confounding factors may have affected the research results, such as smoking, drinking, eating habits, and drug intake.In conclusion, our results showed that MHR was correlated with the severity of early NMOSD and could further predict disease relapse. This new biomarker, which is simple, reliable, economical, and easy to obtain, can also be used for NMOSD risk assessment in clinical practice and may facilitate earlier treatment and improvement of prognosis. Further studies are required to elucidate the specific mechanisms underlying the effect of MHR on NMOSD pathogenesis and disease progression.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Ethics Committee of Zhengzhou University. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.JZ and YL contributed to conception and design of the research. YZho, CP, and YZha organized the database. JZ, YL, and HX performed the statistical analysis. JZ and KW conducted regular follow-up of all cases. JZ wrote the first draft of the manuscript. RD, ZG, and YJ undertook the task of revising the manuscript critically. All authors contributed to the article and approved the submitted version.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.


To quantify the association of cigarette smoking, including cigarettes per day and quitting duration, with the risk of different types of stroke morbidity and mortality in the general population, and to clarify the shape of the dose-response relations.Prospective cohort studies and reported on the association between smoking, quitting and the incidence or mortality of stroke were included.All available data were converted uniformly to odds ratios (ORs) and were pooled using random-effects meta-analysis with inverse variance weighting. A dose-response meta-analysis was performed to explore the quantitative relationship between different smoking characteristics and the risk of different pathologic types of stroke incidence.Twenty-five studies with 3,734,216 individuals were included. Compared to never smokers, the pooled ORs of stroke morbidity and mortality were 1.45 (1.24–1.70) and 1.44 (1.23–1.67) among ever smokers and 1.90 (1.55–2.34) and 1.70 (1.45–1.98) among current smokers. The risk of different pathologic types of stroke was also increased among ever and current smokers. There was a significant non-linear dose-response association between the number of cigarette smoking and the risk of stroke incidence. Comparing no smoking, the ORs for smoking five and 35 cigarettes per day were 1.44 (1.35–1.53) and 1.86 (1.71–2.02). Other pathologic types of stroke have a similar dose-response relationship. There was also non-linear dose-response association between the length of time since quitting and risk of stroke. The risk of stroke decreased significantly after quitting for 3 years [OR = 0.56 (0.42–0.74)].The risk of different types of stroke among smokers is remarkably high. Our findings revealed a more detailed dose-response relationship and have important implications for developing smoking control strategies for stroke prevention.https://inplasy.com/inplasy-2020-6-0062/, identifier: INPLASY202060062. Among 240 causes of death, stroke is the second leading cause of death and disability globally and one of the four largest contributors to disability-adjusted life years among neurological disorders , 2. AccoPrevious studies examining the association of smoking with stroke have yielded mixed findings. Smoking has been recognized as a preventable independent risk factor for stroke, with 12.4% of accidental stroke cases being attributable to current smoking behavior , 6. SomeNotably, the effect of smoking is closely related to its dose, and the association of different smoking characteristics with stroke warrants further investigation. A robust relationship between smoking dose and stroke can inform a decision model for doctors so that patients could possibly know how much less they need to smoke each day or how many years they need to quit smoking before experiencing noticeable health benefits. Particularly, a more precise quantification of the association between current and/or former smoking and stroke risk as well as the identification of a possible threshold for the effect remain to be determined. To date, only a few studies have examined the relationship between smoking dose and stroke and find mixed results. For example, one study used a linear model to evaluate the dose-response relationship between stroke risk and cigarette consumption . Other sBased on these existing results, we carried out a comprehensive systematic review of recent prospective cohort studies that reported the effects of smoking on the risk of different pathologic types of stroke. We evaluated the association between cigarette smoking and the risk of different pathologic types of stroke and estimate their dose-response relationship. By synthesizing evidence across studies and accounting for study heterogeneity, we characterize a more refined dose-response relationship that may have important implications for developing smoking control strategies for stroke.For this meta-analysis, we systematically searched the PubMed, Embase, Books@Ovid, Journals@Ovid, Your Journals@Ovid, Joanna Briggs Institute EBP, ACP Journal Club, CCTR, CDSR, CCA, CLCMR, DARE, CLHTA, CLEED, AMED, Ovid Emcare, HAPI, HealthSTAR, and Ovid MEDLINE(R) databases for studies written in English and published prior to July 31, 2021.The search terms included words associated with stroke and the Cochrane Tobacco Addiction Group search strategy. The full search criteria are listed in Articles were included if they were a prospective cohort study and provided relative risks (RRs), ORs or hazard ratios (HRs) as well as 95% confidence intervals (CIs) for the association between cigarette smoking status and stroke. Cigarette smoking status describes the status at baseline, including never and ever smoking (ever smoking includes both former smokers and current smokers). Studies that involved participants who smoked different amounts of cigarettes or who reported different lengths of time since smoking cessation were also acceptable.Studies were excluded if they set an inexact definition of stroke or included some disease endpoints other than stroke. Compared with spontaneous intracerebral hemorrhage, traumatic hemorrhages, such as subdural and epidural hematomas and other types of intracranial bleeding that are not caused by a vascular event but due to injury, have different mechanisms, courses and outcomes. With the rapid development of technology, there is no longer a clear timeline of diagnosis between stroke and transient ischemic attack (TIA). However, they are still two different diseases because TIA leaves no permanent neurological deficit. Therefore, we only included ischemic stroke (IS) and hemorrhagic stroke (HS)  and subarachnoid hemorrhage (SAH)). Traumatic hemorrhage and TIA should not be characterized as stroke and were not included in this meta-analysis.We refer to the definition of stroke incidence by the American Heart Association and the American Stroke Association in 2013 and ICD codes . In the The follow data were extracted using a standard table: authors, year of publication, inclusion and exclusion criteria, sample size, study population , definition of smoking, smoking status , multivariate-adjusted OR, HR,or RR with 95% CIs of stroke for each smoking status category and follow-up time. This meta-analysis evaluated the correlation between cigarette smoking and the incidence or mortality of stroke, including different pathologic types of stroke, by pooling multivariate-adjusted ORs, RRs, and HRs. Multivariate adjustments were allowed to vary by study but must include age.We evaluated the quality of the included studies using the Newcastle–Ottawa Scale (NOS) for cohort studies. Using this 9-point scale, high-quality studies were defined as a score of 7 or greater; moderate-quality studies were defined as 3–6 points; and low-quality studies were defined as below 3 points. If there were disagreements between the two authors (LW and SG) in the data extraction or quality assessment process, a third author (HW) was consulted for consensus.0) + (P0 × OR)], in which P0 was the event incidence in the control group. We converted RRs into ORs directly when studies did not provide P0 because the incidence or mortality of stroke in the study population is always low (<10%) . In our study, HRs and RRs converted to ORs; HRs were considered as RRs; RRs could be converted into ORs using the formula RR = OR/[(1-Pw (<10%) . MultivaWe performed a dose-response analysis of cigarette smoking or quitting duration on stroke risk by Stata13.1 . The distribution of cases, person-years and the adjusted OR with 95% CI for at least three exposure categories were required. We chose the midpoint of the interval when cigarette number or quitting duration categories intervals were presented. When the upper level for the highest category was open-ended, the exposure doses were calculated as 1.5 times their exposure levels . A potenpost-hoc multiple regression analysis adjusting for risk of stroke at baseline, type gender, length of follow-up and country. To evaluate the robustness of the results, leave-one-out sensitivity analyses were conducted for the primary outcome. We also conducted sensitivity analysis by excluding non-high-quality studies based on NOS scores.To explore factors associated with study heterogeneity, we conducted univariable random effects meta-regression for each of the outcomes when there were at least ten studies available for analysis. We also conducted a Finally, 25 studies –42 were P < 0.00001) for ever smokers, 1.06  for former smokers and 1.90 (1.55–2.34 P < 0.00001) for current smokers compared with never smokers (P < 0.00001), 1.10 , and 1.70 , respectively (P < 0.0001), 1.05 , and 2.09 , 1.01  and 2.58 , 0.97  and 1.61 , 1.23 , and 3.39 , respectively. Although the incidence of SAH was the lowest in the above categories of stroke  and risk of stroke incidence . The continents from which people came had no correlation with the risk of stroke incidence . However, the follow-up time significantly modified the association between cigarette smoking and the risk of stroke incidence , HS (p = 0.90), ICH (p = 0.73) or the mortality of stroke (p = 0.09) in the subgroup analysis. Therefore, the effects of smoking on the risk of stroke may be reversed by the body's strong repair ability.According to the results of the dose-response analysis of quitting, we found that the risk of stroke and IS drop rapidly in the first three years of quitting. This means that the tendency to decrease thrombosis and cerebral perfusion and the negative effects on changes in hemodynamics function and thrombosis caused by smoking might be reversed by the third year. Atherosclerosis from smoking can also be repaired by the human themselves, but it takes more than ten years to show up in a reduction of the stroke risk, and the longer the time, the greater the effect. The risk difference between former and never smokers was not statistically significant in the incidence of stroke  and the number of cigarettes smoked. We also dynamically analyzed the relationship between length of smoking cessation and risk reduction, which could provide clearer guidance and stronger confidence to quitters and potential quitters. Moreover, we limited the ICD codes of the included study and removed age restrictions for a more comprehensive analysis of stroke. Compared to previously published studies, our study included more detailed analyses to support our findings, which leads to new insights.The most important point among these factors was that we observed a significant nonlinear dose-response association between CPD and quitting and the risk of each stroke type  incidence. Hackshaw et al.  used a lIt is also worth mentioning that part of the stroke cohort studies that did not use accurate stroke definitions and ICD codes, equated stroke directly with cerebrovascular diseases . Some of them may also include TIA. All previous meta-analyses on smoking and stroke did not exclude research with inaccurate definitions. This led to an inaccurate number of stroke events and an overestimation of stroke risk. Our study set the inclusion criteria to bring our results closer to the real relationship between smoking and stroke risk.Furthermore, Mons et al.  reportedThis study has several limitations. Because some articles were excluded due to inexact definitions of stroke and ICD codes, the data related to smoking and outcomes for IS, ICH, and SAH mortality are sparse. We were unable to assess dose-response analysis between smoking, quitting and the mortality of different pathologic types of stroke.In addition, heterogeneity across studies was high among the included studies and may be due to different study designs and characteristics of participants. For example, the sample size, follow-up time, multiple adjustments and definitions of never, former and current smokers varied widely from study to study. However, sensitivity analyses showed that any meta-analysis did not alter the pooled OR significant, and the pooled results were stable.Due to its high morbidity, mortality and disability rate, stroke has brought a heavy burden to modern society in different aspects. At present, the stroke population is still increasing, for example in China, and carrying out primary stroke prevention, such as reducing smoking, is the best way to prevent stroke . TherefoSmoking will increase the risk of stroke with different pathologic types. There was a non-linear dose-response relationship between the amount of cigarette smoking and duration of cessation and stroke risk. Low-dose smoking can carry half or more of the additional risk from large doses of smoking. Quitting smoking for more than 3 years will deliver significant health benefits. Our findings provide a more detailed dose-response relationship and have important implications for developing smoking control strategies for stroke.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.LL developed the study concept. JL and XT did the literature search, article screening, data extraction, and statistical analyses. HW, LW, and SG evaluated the quality of studies. FL, CT, NX, and LL directed research and revised manuscripts. All authors were involved in drafting the manuscript.This work was supported by the Key-Area Research and Development Program of Guangdong Province (2020B1111100008 to LL), the Youth Scientific Research Training Project of GZUCM (2019QNPY02 to LL), the Young Top Talent Project of Scientific and Technological Innovation in Special Support Plan for Training High-level Talents in Guangdong (2017TQ04R627 to LL), the Youth Program of the National Natural Science Foundation of China (81904297 to LW), the Elite Youth Education Program of Guangzhou University of Chinese Medicine (QNYC20190106 to LW), and the Science and Technology Planning Project of Guangdong Province (2014B090902002 to NX). The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.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.


In this study, restorative environment theory and virtual reality (VR) technology were combined to build different 3D dynamic VR interactive scenes. We discuss the effects of a VR restorative environment on the emotional and cognitive recovery of individuals with mild-to-moderate anxiety and depression. First, we built a VR restorative garden scene, divided into four areas: forest, lawn, horticultural planting, and water features. The scene was verified to have a good recovery effect in 26 participants. Then, 195 participants with mild-to-moderate anxiety and depression were selected as experimental subjects. Through psychological testing and EMG (Electromyography) and EEG  data feedback, we further explored the differences in the sense of presence in VR restorative scenes and their effect on individual emotional and cognitive recovery. The results showed that (1) both the restorative environment images and the VR scenes had a healing effect (the reduction in negative emotions and the recovery of positive emotions and cognition), with no difference in the subjective feeling of recovery among the different scenes, but the recovery score of the VR urban environment was higher than that of the natural environment ; (2) a high sense of presence can be experienced in different VR scenes, and interactive activities in VR scenes can provide a great presence experience; (3) the recovery effects of VR restorative environment on emotion and self-efficacy are realized through the presence of VR scenes; (4) a VR restorative environment is helpful for the emotional improvement and cognitive recovery of individuals with mild-to-moderate anxiety and depression. VR urban scenes also have good recovery effects. In terms of cognitive recovery, self-efficacy improved significantly. In addition, from the perspective of EEG indicators, the VR restorative scene experience activated the prefrontal lobe, which is conducive to cognitive recovery in individuals with mild-to-moderate anxiety and depression. In terms of emotional improvement, negative emotions were significantly reduced in the different VR scene groups. In conclusion, we further explored ways to help individuals with mild-to-moderate anxiety and depression, in order to promote the development and application of mental health. With rapid urbanization, the attention of individuals is consumed and pressure increases are experienced due to a high intensity of study and work, continuously affecting their physical and mental health ,2. This The restorative environment concept was first put forward by environmental psychologists , which rThe theoretical basis of the research on the restorative environment includes attention restoration theory (ART) proposed by Kaplan and stress reduction theory (SRT) proposed by Ulrich, also known as psychological evolution. ART points out that a task that requires psychological resources will arouse directional attention. If the duration and intensity of the task reaches a certain degree, even if the target is pleasant, it can also cause mental exhaustion. Natural scenes can provide active involuntary attention to stimulation, causing the attentional system to resume. The theory explains how to attract involuntary attention to the natural environment and how to recover cognitive ability. SRT states that, when individuals under stress are exposed to medium complexity, visual focus, depth, mystery, no potential threat, uniform ground texture, deflecting visual field, and environments containing plants and water, their attention will be attracted, thus blocking negative thoughts, and so, their emotions can change from negative to positive. The disturbed physiology returns to a balance, and so does cognitive behavior. These two theories have different focuses in terms of concept and mechanism. The former focuses on fatigue in order to explain the recovery of individual attention resources and the change in cognition during natural experiences, while the latter focuses on the individual emotional response to nature. In recent years, the two theories have shown a shift in trend from opposition to integration . Hartig elaborated on restoration, which refers to regaining the physical, psychological, and social abilities that are lost in the process of adapting to the external environment . RecoverResearch on rehabilitation of anxiety and depression in virtual reality is very common. Presenting virtual fear stimuli in exposure therapy to treat anxiety, using immersive games to distract patients to relieve their pain, or presenting a natural environment to achieve relaxation ,43,44 arThe measurement of environmental resilience includes subjective reporting and objective measurement. The Perceived Restorative Scale (PRS) covers the emotional, physiological, cognitive, and behavioral dimensions . The ChiWith the arrival of the intelligent era, new technologies represented by VR have gradually come into public view and have improved the quality of human life . VR techIn previous studies, the virtual reality scene was in the form of video materials ,76, 360°VR is a type of completely immersive computer simulation experience utilizing a three-dimensional interactive virtual environment. A VR system consists of a computer, a head-mounted display, headphones, and motion-sensing gloves . CompareSheridan proposed the development of a scientifically useful presence metric and determined its significance and value in remote operation applications . Mel andThe current research on VR restorability mainly focuses on urban and natural scenes with the natural elements and natural analogues and experience of space and place . StudiesOn the basis of previous studies, we explore the common recovery theories (ART and SRT), which have been predominantly tested on the cognitive and emotional recovery of individuals in former studies. Moreover, researchers investigate the positive effect of a restorative environment based on VR. However, how to design restorative virtual scenarios based on recovery theories is unclear and the effectiveness of VR scenarios is of great interest to the interdisciplinary research field . Therefo(1)The 2D images of initial restorative scenes used in this study have recovery effect.(2)The VR restorative scenes we used have recovery effect.In this part, a restorative VR environment is designed and realized, the purpose of this study was to verify its recovery effect, to select the experimental materials for the next step from it. Based on the results of previous study of restorative environment, images and VR scenes of restorative environment have healing effects. The hypotheses were made for Study I:Based on environmental psychology, landscape design, rehabilitation medicine, and other relevant materials, appropriate environmental elements were selected to construct a restorative garden, which included seven environmental zones: forest area, meditation area, flowers area, lawn area, gardening area, rest and interaction area, and water scenic area. The researchers used hand-drawn images and PS , AI , CAD 2019 , Sketchup 2019 , and other design software to draw the scenes and invited experts to evaluate and modify the environmental elements, paths, colors, and so on. After this, Lumion 8.0 software  was used to present the preliminary restorative virtual reality environment. Finally, the interactive development system of Unity was utilized to construct an immersive VR environment, in which users could freely roam within the limited space.nmale = 4) from Beijing Forestry University, with an average age of 18.80 years, who were majoring in applied psychology, law, civil engineering, and so on. After a normality test, data from eight participants were included in the analysis.The initial study participants were 10 undergraduates  was used to evaluate the restoration effect of the VR scenario. This scale was compiled based on ART theory and is the first Chinese version of the RES, which can be divided into three dimensions: being away, fascination and compatibility, and abundance. The Cronbach’s α coefficients of the total scale and the three sub-scales ranged from 0.769 to 0.936, and the split reliability distribution ranged from 0.695 to 0.903, indicating high reliability and validity.® CoreTM i7-7500U@2.70 GHz; GPU: NVIDIAGeForce940MX, Asustek Computer Inc., Shanghai, China) computer was used to display the pictures captured from the scenes.An Asus PU554U  software showed that the average score (standard deviation) of the recovery environment, with scores of 4.06 (1.00) for being away, 5.29 (1.08) for fascination and compatibility, and 2.72 (1.23) for abundance. The results indicate that the initial restorative scenes had a certain recovery effect, in which the characteristics of being away, attraction, and compatibility were better than the characteristic of abundance; the experimental effect may be affected by the number of participants and fewer viewing materials. In addition, in this experiment, we only presented two-dimensional pictures, such that the experience of the participants may differ from that in VR, meaning further exploration should be carried out using VR equipment. The presentation of scenes should be combined with the technical realization effect, which was suggested to improve the richness of scenes, and to select forest, lawn, horticultural planting, and waterscape as the main environmental partition.nmale = 9) from Beijing Forestry University, with an average age of 22.31 years, majoring in applied psychology, landscape architecture, computer science, and engineering. After completing the experiment, the participants were paid 20 yuan.The participants of the second experiment were 16 undergraduate students  = 0.696, p > 0.05), but the means of three dimension scores (tbeing away(15) = 2.335, p < 0.05; tattraction and compatibility(15) = 2.668. p < 0.05; trich(15) = −3.996, p < 0.01) were significantly higher than the average score (4). We found that the VR restorative environment, in terms of being away, fascination and compatibility, and abundance, was better than viewing 2D images.The IBM SPSS STATISTICS 23.0 software was used for statistical analysis, and the data of the total scale and three sub-scales of the RES were found to be normally distributed. The average score for the restorative environment was 4.11 (0.65), while it was 4.63 (1.07) for being away, 4.75 (1.12) for fascination and compatibility, and 2.96 (1.04) for abundance. Taking the mean of the seven-point scale (4) as the comparison standard, an independent samples To further investigate the differences in the presence of VR environments and their interactive activities, and to verify their effects on individual emotional and cognitive recovery, 20 participants were recruited to conduct a pre-experiment, for which five scenes were identified: a watering task in the garden area and fishing task in the waterscape area with better interactive experiences, controller-free and controller garden scenes, and a VR urban environment (which was added as a control group). These five virtual scenes were used as an intervention in individuals with mild-to-moderate anxiety and depression, as well as to discuss the effect of a VR restorative environment, the sense of presence, and its effects on the emotions and cognition of individuals, through scale measurements and relevant physiological data.(1)There will be differences in subjective restoration and the sense of presence of different VR restorative scenes. The subjective restoration of VR restorative scenes will be higher than that of VR urban scene. The presence of VR restorative environment with interaction will be better than that in other intervention groups.(2)Different VR restorative environment experiences will have different healing effects on the degree of change in individual emotions and self-efficacy for individuals with mild-to-moderate anxiety and depression.(3)VR restorative scenes (Env2~Env5) will contribute to improve positive emotions, reduce negative emotions, and improve self-efficacy of individuals with mild-to-moderate anxiety and depression. The VR urban scene (Env1) will have the opposite effect to VR restorative scenes. Different VR restorative environment experiences will have different healing effects on the directions of change in individual emotions and self-efficacy.(4)The recovery impact of the VR rehabilitative environment on people was probably realized through the presence of VR scenes.(5)The differences of presence will also be reflected in EMG: compared to the baseline, indicators of physical participation  in the VR scene experience will improve.(6)VR restorative scenes will be conductive to the cognitive recovery of individuals with mild-to-moderate anxiety and depression, which will be reflected in EEG indicators: prefrontal alertness and engagement will be increased, and the calming signal index will be decreased in the VR restorative environment experience.The purpose of this study was to explore the intervention effect of the presence of a VR restorative environment on individuals with mild-to-moderate anxiety and depression from the perspective of emotion and cognition. Based on the results of Study I and the pre-experiment, further hypotheses were made for Study II:nmale = 76; nfemale = 113) were eligible for the experiment, with an average age of 20.26 years (2.58).A total of 369 undergraduate and graduate students from Beijing Forestry University and Beijing Normal University were recruited, with normal vision or corrected vision, no major physical or mental diseases, and good attention and executive ability. The screening criteria were state anxiety and depression scale scores ≥40 and ≤60. Among them, 195 were eligible to participate in the experiment, and 189 valid data were obtained. A total of 189 participants (n1 = 39), a VR restorative environment visual experiencing group , a VR restorative environment interactive experiencing group , a VR restorative environment with fishing interaction group , and a VR restorative environment with watering interaction group . The participants earned 10 yuan after completing the experiment.The participants were divided into a VR urban environment visual experiencing group , VR restorative environment visual experiencing (Env2), VR restorative environment interactive experiencing (Env3), VR restorative environment with fishing interaction (Env4), and VR restorative environment with watering interaction (Env5) groups, for a total of five experimental groups. This study was carried out in October 2020, following approval from the Ethics Committee of the Department of Psychology, College of Humanities and Social Sciences, Beijing Forestry University.The experimental materials and instruments were as follows:VR scenes constructed using the Unity interactive development system—urban and restorative environments . Screenshots of the scenes are shown in VR equipment: An HTC Vive Pro Eye set , including one helmet, two handles, two 2.0 locators, two locator brackets, and a data cable.® CoreTM i7-8700k@3.70 GHz; GPU: NVIDIA GeForce GTX 1080 Ti, Dell, Inc., Round Rock, Texas, USA).Computer equipment: Alienware , Inc MP160 physiological permeameters: Used to detect and record the electrical activity during the process of the intervention. An EMG100C amplifier was required for EMG signal acquisition, along with two LEAD110S shielded conductors, one LEAD100A unshielded conductor, and three disposable patch electrodes. An EEG100C amplifier was required for EEG signal acquisition, along with two Lead110 shielded leads, one Lead100 unshielded lead, and three disposable patch electrodes.The Restorative Environmental Scale (RES): the same as used in Study I.The Presence Questionnaire: Composed of 29 items, including involvement, sensory authenticity, adaptation, and interface quality . It has State–Trait Anxiety Inventory (STAI): The anxiety of participants was assessed with a 40-item scale, on a scale of 1–4: questions 1–20 comprised the State Anxiety Inventory , while questions 21–40 comprise the Trait Anxiety Inventory ; the former describes short-term unpleasant emotional experiences , often accompanied by vegetative nervous system function, while the latter is typically used to describe the relatively stable anxiety tendency as a personality characteristic with individual differences .The Self-Rating Depression Scale (SDS): Contains 20 items and is rated from 1 to 4. It is easy to use and can fairly intuitively reflect the symptoms, severity, and changes in the depression state .The Positive and Negative Affect Scale (PANAS): Used to assess the positive and negative emotions of participants. Chinese scholars have conducted a study on the applicability of PANAS among the Chinese population  and intrGeneral Self-Efficacy Scale (GSES) (Chinese version): Used to evaluate self-efficacy, including 10 items. The Chinese version has good cross-cultural applicability, reliability, and validity .The experimental procedure is shown in After understanding the experimental content and signing an informed consent form, the participants began the experiment, filled out the Positive and Negative Affect Scale and the General Self-Efficacy Scale, and were then equipped with the MP160 physiological equipment. First, we wiped the skin surface of the participants with alcohol and normal saline, then pasted on the disposable patch electrode. For EMG measurements, positive and negative EMG input signals were placed on the right wrist, the flexor carpi radialis muscle, and a GND (Ground) electrode was placed on the elbow to measure the brachioradialis muscle EMG signals. For EEG measurements, positive and negative input signals were placed on the left and right sides of the forehead. A GND electrode was placed behind the ear, at the mastoid of the temporal bone. After setting the original channel and collection parameters, we started collecting EMG and EEG signals. After wearing the physiological equipment, participants were required to sit on a swivel chair and conduct breathing relaxation training to help them stay at rest and relaxed. Baseline physiological data collection was conducted, which was suspended after 3–5 min. Then, the participants wore the VR equipment and adjusted the tightness of the helmet to ensure that their vision was clear, after explaining the required operations in the virtual scene (only for the interactive activity group) to the participants  = 1.594 and p = 0.179. There were no inter-group differences in the scores of the recovery environment under different activity conditions, which indicates that there were no significant differences in the subjective feelings of recovery in the participants across the five experimental scenarios. There were no significant differences in the environmental recovery of the VR scenes or their dimensions; however, from the perspective of the descriptive statistical results, the environmental recovery of the VR urban environment visual experience group scored the highest. In terms of being away, attraction, and compatibility, the VR restorative environment visual experience group was the best, while, in terms of the richness dimension, the VR healing environment interactive tour group was the best.One-way ANOVA was used to compare the differences in the scores of the participants during the recovery environment under different activity conditions, consistent with the hypothesis of homogeneity of variance. The ANOVA results were The presence questionnaire data were tested for normality. The absolute values of z-score of kurtosis and skewness were all less than 1.96, and the data were found to be normally distributed. The presence scores for each scene are shown in F (4161) = 5.036 and p = 0.001, and there were significant differences in the sense of presence for the different scenes.One-way ANOVA was adopted. The homogeneity of variance test results showed that the sense of presence of each experimental group was in line with the hypothesis of homogeneity of variance. The one-way ANOVA results for the sense of presence of the five groups were t(1) = 3.604, p < 0.001), Env2 and Env3 (t(1) = 3.648, p < 0.001), Env3 and Env4 (t(1) = −3.218, p < 0.01), and Env3 and Env5 (t(1) = −3.239, p < 0.01).The scores of each group’s experienced scenes were LSD-tested, as shown in PROCESS was adopted to analyze the mediating effect, and Model 4 was selected as the mediating effect model.The results were shown in For data analysis, we excluded outliers  from the positive and negative emotional and self-efficacy scores of the five experimental groups before and after experiencing VR scenes. The skewness and kurtosis of the data of positive and negative emotions and self-efficacy were examined. The original data did not conform to the normal distribution, so logarithmic transformation was performed on the data to ensure that the data were in line with a normal distribution or were approximately normally distributed. F (1141) = 6.984 and p = 0.009. The main effect of experience scene was not significant, F (4141) = 0.560 and p = 0.692. The interaction was not significant, F (4141) = 1.562 and p = 0.188. For negative emotions, there was significant difference between pre- and post-test, F (1141) = 63.215 and p < 0.001. The main effect of experience scene was not significant, F (4141) = 0.758 and p = 0.554. The interaction was not significant, F (4141) = 0.391 and p = 0.814. For self-efficacy, there was significant difference between pre- and post-test, F (1141) = 23.593 and p < 0.001. The main effect of experience scene was not significant, F (4141) = 0.359 and p = 0.837. The interaction was not significant, F (4141) = 0.899 and p = 0.466. These results indicated that VR scene intervention does have an impact on participants, and the difference between different scenes was not significant.Repeated measurement ANOVA for positive emotion, negative emotion, and self-efficacy was used, respectively, to compare the differences in the scores of the participants during the restorative environment under different groups. For positive emotions, there was significant difference between pre- and post-test, t-test was conducted for the positive and negative emotional and self-efficacy scores of the five groups before and after experiencing the VR scenes. The results are shown in In order to further clarify how the emotional and self-efficacy score of the participants changed after the scene experience, whether it was significantly increased or decreased, and whether there were different directions of change between different scenes, a paired samples All of the five virtual scenes significantly reduced negative emotions. The VR restorative environment interactive experience significantly decreased positive emotions. The VR restorative environmental visual experience and VR restorative interactive fishing and watering activity experiences significantly improved self-efficacy. We confirmed the hypothesis that different VR restorative environment experiences have different healing effects on the directions of change in individual emotions and self-efficacy. Among them, the VR urban environment visual experience significantly reduced negative emotions, but did not have a significant impact on individual positive emotions and self-efficacy, which was inconsistent with our expectations.The collected EMG and EEG signals were processed digitally using AcqKnowledge 5.0 software . First, a comb filter was used to set the fundamental frequency of 50 Hz, an IIR recursive filter was used for preliminary filtering, and low-to-high-pass EEG filtering was set in the range of 1–40 Hz. The low-to-high-pass EMG filtering was set in the range of 1–500 Hz, and the EMG and EEG signals after initial noise reduction were obtained. Furthermore, the noise caused by errors in the experiment was manually deleted. Meanwhile, MATLAB 2019A software was used for offline denoising and analysis of the EMG and EEG signals, and the time and frequency domain characteristics of the EMG signals, as well as the average power and ratio of the power spectral density of the EEG signals, were obtained.p < 0.001), MAV, Z = 7.818 (p < 0.001), MPF, Z = 4.800 (p < 0.001), and MF, Z = 2.698 (p < 0.001). The significant differences between the time and frequency domain eigenvalues of EMG in the five groups before and during the scene experience are shown in The pre-processed EMG signals were extracted for features. The time domain eigenvalues were the root mean square (RMS) and the mean absolute value (MAV), and the frequency domain eigenvalues were the mean power frequency (MPF) and the median frequency (MF), reflecting contraction of the brachioradialis muscle of the participants’ arm, which was used as a body participation index reflecting the VR scene experience. Partly due to the relatively small sample size and the normal test of experimental data showing that they did not follow a normal distribution, the scene before the experience and the experience in the process of electromyographic signal characteristics were separately subjected to the Wilcoxon signed rank test, and we obtained the results: RMS, Z = 7.818 (χ2(4) = 6.295 (p = 0.178) and MAV χ2(4) = 7.124 (p = 0.130), and the EMG time domain characteristics in the different scenes did not significantly differ: MPF χ2(4) = 14.410 (p = 0.006) and MF χ2(4) = 13.823 (p = 0.008). The EMG frequency domain characteristics were significantly different between the different scene experiences, indicating different degrees of arm muscle activity and body involvement in the different scenes, which supports the results that there were differences in the sense of presence in the different scenes.The EMG time and frequency domain characteristics in the different experience scenes were compared. The results of the K–W one-way ANOVA were RMS low (13–15 Hz), βhigh (23–30 Hz), and θ (4–8 Hz). EEG waveforms for each frequency band are shown in high/βlow, EI = β/(θ + α), and TBR = θ/β, representing alertness, engagement, and calmness, respectively [After the EEG signal was filtered using AcqKnowledge 5.0, the artifacts were removed manually in order to prevent interference from events such as eye movements, large movements of the head and body, and sweating. Then, eye movement artifacts were detected using a sliding window function peak–peak threshold method with MATLAB, and amplitude changes of more than 150 μV were excluded. Drift and other artifacts larger than 100 μV were detected and marked by a cyclic algorithm, then excluded. In order to gather the power spectral density, the Welch method was used to divide the data into 1 s long windows with 50% overlap. The EEG indices of each channel were calculated, and the PSD was divided into α (8–13 Hz), β (13–30 Hz), βectively .p < 0.001), participation , and calmness  indicating that there were significant differences in the EEG index before and during the experience. The measured differences in the power spectral density ratio for the EEG signals before and after the five scenarios are shown in After processing the missing data, the normal test showed that the data did not conform to a normal distribution, such that the use of test methods for the scene before the experience and the experience in the process of brain electrical characteristics, respectively, were through the Wilcoxon signed rank test, with the results for alertness  = 0.808 (p = 0.937); calmness, χ2(4) = 3.116 (p = 0.539). The EEG signals of the different scenes had no significant differences in terms of engagement and calmness. For alertness (χ2(4) = 11.650, p = 0.020), the prefrontal EEG alert state index was significantly different in the different scenes, indicating that there were differences in the prefrontal lobe activity in different scenes. The results of the EEG signal analysis showed that, during the VR restorative environment experience, the alert state and engagement of the prefrontal lobe were significantly increased, while the signal in the calm state was weakened, indicating that, compared to the baseline state, the scene experience brought the prefrontal lobe into an excited state, which indeed contributed to individual cognitive recovery and improved cognitive function.Comparing the EEG indicators when experiencing different scenes, the results of the K–W one-way ANOVA were as follows: engagement, In Study I, we proved that the VR restorative garden scene had a healing effect, consistent with previous research results. Compared to the physical environment, the VR forest environment had the same healing effect and was more attractive and coherent ; howeverIn Study II, participants with mild-to-moderate anxiety and depression mood states were intervened. It was found that there were no significant differences between the different VR restorative environment scenarios in terms of individual subjective healing feeling. This may have been caused by the inter-subject design, which made individuals insensitive to the changes in scene reality and psychological immersion, such that the differences in the perceived restorative resilience of the scenes was not obvious. In addition, it is unknown whether the absence of sound in the VR scenes affected the associated environmental healing. Previous studies have confirmed that natural sound has a strong impact on recovery ,118. SouThere were significant differences in the presence experienced in the different VR scenes, which may have been caused by motion sickness, device perception, personality traits, interaction with VR scenes, immersion, and participation ,122,123.As the results showed, the sense of presence had different mediating effects on different dependent variables. When the independent variable was the score of RES, the mediating effect of presence on negative emotions was the strongest, followed by self-efficacy and positive emotions. Based on these findings, this study proved that the recovery impact of VR rehabilitative environment on people was probably realized through the presence of VR scenes. However, the mechanism of presence has not yet probed in depth in this research, and the impact related to the sense of presence have not fully found and controlled, so its mediating role in the effects of RES on emotions and self-efficacy requires further study.From the results, we determined the effects of the VR restorative environment experience on the positive and negative emotions and self-efficacy of individuals with mild-to-moderate anxiety and depression and found different directions of influence, which preliminarily proved the intervention effect of VR scene experiences on individual emotions and self-efficacy. Previous studies have found that groups with high-anxiety traits have a strong attention bias toward negative information, indicating that they are sensitive to negative information and find it difficult to reject a bad state after locking threats. They are more alert to negative stimuli and pay more attention to their internal world for a longer time, while non-high-anxiety groups tend to pay more attention to happy information. Their lockdown time is also longer ,125,126;When we consider the comparison between different scenes. We did not find differences in individuals with mild-to-moderate anxiety and depression, as we had assumed. However, according to the hypothesis, for those in the visual experience group, who did not need to learn and operate the controller, the task was less difficult than for the interactive experiencing group, and so, their participation and enjoyment were lower than in those who performed the fishing and watering activities; therefore, these factors influence the presence due to VR . Some stWe found that VR visual experiences in urban environments significantly reduced negative emotions, but did not have significant effects on individual positive emotions or self-efficacy. The reason for this discrepancy was that most of the participants recruited for the experiment were students who had been studying or working indoors for a long time. The VR urban scene was different from their pressure environment familiar to the participants, thus meeting one of the characteristics of a restorative environment: the feeling of being far away. Empty urban environments, which are free from crowding and oppression, and experiences without directed attention have a restorative effect . TherefoThree brain function indicators were selected in this study, namely, alertness, engagement, and calmness. Through EEG feedback, differences in the effects of the five experimental scenes on the prefrontal lobe were found, which preliminarily verified the correlation between the VR restorative environment and individual cognitive function recovery. However, the mechanism of its influence is not clear yet. In addition, the EMG results showed that the scene experience enhanced arm muscle contractive activity, reflecting increased body involvement, and that the different interaction groups did not produce cognitive burden.In general, this study proves that the experience of a VR-based restorative environment scene has a good healing effect in people with mild-to-moderate anxiety and depression, where such recovery was reflected in its influence on the positive and negative emotions, self-efficacy, and cognitive function of these individuals. The influencing factors and the internal psychological mechanism of the recovery effect of restorative environments have been widely treated as the research focus of environmental psychologists; however, previous research on restorative environments has mostly been based on real scenes or non-interactive immersive VR scenes. In this study, a restorative environment was achieved by VR technology and 3D dynamic interactive activities, thus enhancing the connection between environment and people, which was lacking in previous studies. The interactive tasks focused on the activities of people in the environment, which enhanced their interests and avoided the adverse emotional reactions caused by a lack of interaction for a long time and prevented its effects on physiological indicators. On the basis of this innovation, research on the connection between the environment, behavior, and psychological experience can be supplemented to explore the differences in healing and the presence of experience due to different scenes, as well as the influence of restorative environments on individual emotions in VR scenes. We also used EEG feedback to prove the effect of the VR restorative environment on cognitive recovery, serving as a useful supplement to the research of restorative environments on individual cognitive recovery.In theory, this study was a theoretical exploration, and the extension of research on the restorative environment will help to improve the understanding of the internal mechanism of the recovery due to the restorative environment. At the same time, it provides a theoretical basis for the practical application of environmental recovery. VR technology has a good restorative function and application value when people cannot access natural environments with high recovery potential . This teIn this study, we proved that VR restorative scenes have a certain healing effect on mild-to-moderate anxiety and depression, but have the following shortcomings. First of all, considering the integration of psychological and VR technology, the attention, focus, and cognitive preference in the psychosocial response due to specific anxiety and depression moods have not yet been fully grasped. We failed to discuss the possible effects of attention , environSecond, natural sound is one of the factors affecting the efficacy of restorative environments , as the Third, the improvement of anxiety and depression is a long-term endeavor. In this study, we tested the participants only once. In next research, we can discuss whether this kind of intervention can benefit the individuals with mild-to-moderate anxiety and depression in a long-term way.In future research, we may explore the regulating effect of the sense of presence on the mental and physical healing effects due to VR restorative environments, as well as further explain the associated internal psychological mechanism. In summary, future research on restorative environments based on the assumption that researchers can comprehensively consider the psychological needs, physiological responses, cognitive patterns, operating habits, and environmental preferences of users in the VR environments, as well as discuss how to improve the recovery effect of VR environments and apply it in practice.There was no significant difference in the healing effect between different VR scenes, but the restorative score of the VR urban scene was higher than that of the VR natural environment.A high sense of presence could be experienced in different VR scenes, and interactive activities in VR scenes can provide a great presence experience. However, roaming in a natural environment through controller operation had the lowest sense of presence. The differences of presence were also reflected in EMG.The recovery effects of VR restorative environment on emotion and self-efficacy are realized through the presence of VR scenes.VR restorative environments are helpful for emotional improvement and cognitive recovery in individuals with mild-to-moderate anxiety and depression. VR urban scenes also have good recovery effects. In terms of cognitive recovery, self-efficacy improved significantly. In addition, from the perspective of EEG indicators, the VR restorative scene experience activated the prefrontal lobe, which is conducive to cognitive recovery in individuals with mild-to-moderate anxiety and depression. In terms of emotional improvement, negative emotions were significantly reduced in the different VR scene groups.Considering the results of this study, we drew the following conclusions: