The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing in parallel with the epidemic of obesity and metabolic syndrome. Lifestyle modification is a crucial strategy for the treatment of NAFLD, which can lead to a reduction in liver fat with concomitant weight loss. The use of eHealth technologies is an effective approach to improve health outcomes in patients as they do not have any time and space limitations.This study aimed to evaluate published eHealth intervention studies for the improvement of lifestyle modifications among patients with NAFLD and to provide recommendations for future studies.We conducted a systematic review and meta-analysis. Five electronic databases  were searched for studies reporting the effect of lifestyle modification intervention using eHealth in patients with NAFLD published from inception to November 3, 2022. Study selection, data extraction, and quality assessment were performed by 3 researchers independently. The quality of included studies was assessed using the Cochrane risk of bias tool and the Risk of Bias Assessment Tool for Nonrandomized Studies.P=.02; 95% CI –1.01 to –0.10), AST , and ALT  were observed in the eHealth intervention as compared with the control groups.In total, 2688 records were identified, and 41 full-text articles were assessed. Seven studies were included in the systematic review. The participants of all interventions were 1257 individuals with NAFLD, and the mean age ranged from 38.3 to 57.9 years. The duration of the interventions was 3-24 months, and all interventions were categorized into 3 types: internet-based computers, telephones, and mobile apps. Of these, 4 studies were randomized controlled trials and were included in the meta-analysis: 3 studies for body weight and BMI and 4 studies for alanine aminotransferase (ALT) and aspartate aminotransferase (AST). According to the meta-analysis, clear improvements in BMI (Lifestyle modification interventions using eHealth technologies are significantly effective for BMI, AST, and ALT in patients with NAFLD. Future research should conduct interventions with larger sample sizes and evaluate whether these interventions have sustained benefits, and how we can make these eHealth methods most effective on a large scale. Nonalcoholic fatty liver disease (NAFLD) is a growing common cause of chronic disease. The global prevalence of NAFLD, which is associated with metabolic syndrome or other chronic diseases such as diabetes, hyperlipidemia, and hypertension, is approximately 33% . NAFLD iNAFLD is usually asymptomatic in the majority of patients and diagnosed by imaging or histology. Since NAFLD is a progressive disease , patientThe treatment of NAFLD has focused on lifestyle modifications related to dietary habits and physical activity leading to body weight loss ,7. The cPrevious studies have conducted lifestyle interventions of various durations and strategies, and several studies have reported positive effects -11. HoweeHealth is defined as health services and information delivered or enhanced through the internet and related technologies . As an iThis systematic review aimed to evaluate the effectiveness of lifestyle modification interventions, using eHealth, in experimental trials involving adults with NAFLD. Additionally, the review compares these eHealth interventions with usual care in terms of key clinical outcome variables.This systematic review and meta-analysis was conducted using the Cochrane Handbook for Systematic Reviews of Interventions and was reported following the PRISMA  statement guidelines . This reWe conducted a systematic literature search of the PubMed, Cochrane Central, Embase, CINAHL, and Web of Science databases on November 3, 2022. The search strategy included the following keywords as Medical Subject Headings (MeSH) or Embase Subject Headings terms:  AND (“lifestyle modification” OR “diet” OR “exercise” OR “weight loss” OR “body mass index”) AND . For example, the search strategy in PubMed is presented in Nonalcoholic fatty liver“non-alcoholic fatty liver” [MeSH Terms] OR “non-alcoholic fatty liver” OR “nonalcoholic fatty liver” OR “non-alcoholic fatty liver” OR “NAFLD” OR “nonalcoholic steatohepatitis” OR “NASH” AND “fatty liver” [MeSH Terms] OR “fatty liver” OR “hepatosteatosis” OR “hepatic steatosis.”Diet, exercise, weight loss, BMI, and lifestyle modification“diet” [MeSH Terms] OR “diet” OR “nutrition” [MeSH Terms] OR “nutrition” OR “food intake” [MeSH Terms] OR “food intake” OR “exercise” [MeSH Terms] OR “exercise” OR “physical activity” OR “weight loss” [MeSH Terms] OR “weight loss” OR “body weight” [MeSH Terms] OR “body weight” OR “body weight change” [MeSH Terms] OR “body weight change” OR “lifestyle modification” OR “lifestyle change” OR “body mass index” [MeSH Terms] OR “body mass index” OR “BMI.”eHealth“telemedicine” [MeSH Terms] OR “telemedicine” OR “mhealth” OR “mobile health units” [MeSH Terms] OR “mobile health units” OR “cell phone” [MeSH Terms] OR “cell phone” OR “smartphone” [MeSH Terms] OR “smartphone” OR “satellite phone” OR “computers, handheld” [MeSH Terms] OR “computers, handheld” OR “tablet computer” OR “mobile application” OR “mobile app” OR ”apps” OR “eHealth” OR “computers” [MeSH Terms] OR “computers” [All Fields] OR “internet-based intervention” [MeSH Terms] OR “internet-based intervention” OR “internet” OR “internet-based” OR “web-based” OR “text messaging” [MeSH Terms] OR “text messaging” OR “telehealth.”We included all interventional studies published from inception to November 3, 2022, limited to peer-reviewed articles that were written in English and conducted among humans. We considered the inclusion of literature in this review if it met the following criteria: (1) it dealt with all adults (18 years and older) diagnosed with NAFLD using sonography or transient elastography ; (2) any eHealth interventions for lifestyle modification related to diet or physical activity, delivered through an electronic device with internet connectivity or wireless capacity, were used; and (3) outcomes were reported as any quantifiable measure for evaluating the effect of lifestyle modification .The exclusion criteria were as follows: (1) if patients with NAFLD were not the main participants, (2) if NAFLD was not the primary diagnosis, (3) if interventions were only targeted at patients with NAFLD, and (4) if only eHealth techniques were used for evaluating the outcomes.Study citations were imported and compiled into reference management software  for selection. Studies were screened and selected by 3 reviewers . For the initial search, after removing duplicates, 2 researchers (JC and OYK) independently screened the titles and abstracts of the studies identified from 5 electronic databases to remove irrelevant studies, using eligibility assessment lists. In the second phase, the researchers checked the study types of all the remaining studies, and the full texts of potentially relevant articles were retrieved. Any disagreements or discrepancies between the 2 authors (JC and OYK) regarding the inclusion of studies were resolved through discussion with a third reviewer (YJ).Two researchers (JC and OYK) extracted and cross-checked the data independently. The following information was extracted from each selected study: author, year of publication, study location, intervention and control groups , details about intervention and control , and relevant outcomes and results. If the studies reported that data were insufficient for meta-analysis, the lacking data were requested from the corresponding authors. All the researchers checked the extracted data for consistency. Discrepancies were resolved through discussions.Descriptive analyses of the data were performed, and the findings were synthesized based on the category of outcomes, intervention type and content, and its effect. The primary outcomes of this review were weight, BMI, and liver enzyme levels. When an original study reported only the total sample size of the follow-up data without the number of each control and intervention group, we estimated the sample size of each group using pooled standard deviation formula with pooled SD of each group and 95% CI .Meta-analysis of the extracted data was conducted using the Review Manager of the Cochrane Collaboration . Standardized mean differences (SMDs) and corresponding 95% CIs were computed as between-group differences in means divided by pooled SDs with Hedges correction . The CocI2 heterogeneity degree, wherein 20%, 50%, and 75% were considered indicative of low, medium, and high heterogeneity, respectively [I2 heterogeneity degree, was greater than 50% and P<.1, we performed random-effects analysis.Heterogeneity within studies was calculated using the ectively . We usedThe quality of included studies was assessed by using the Cochrane Collaboration Risk of Bias Tool (RoB II) for included randomized controlled trials (RCT) . For nonFor RCTs, the following criteria were considered: (1) the randomization process, (2) deviations from intended interventions, (3) missing outcome data, (4) measurement of the outcome, and (5) selection of the reported result. Each domain was evaluated using the Cochrane risk ratings of “low,” “high,” and “some concerns.” The criteria considered for the quasi-experimental trial included (1) the selection of participants, (2) confounding variables, (3) the measurement of exposure, (4) blinding of the outcome assessments, (5) incomplete outcome data, and (6) selective outcome reporting. Risk ratings of “low,” “high,” and “unclear” were assigned for each bias.Our initial search identified 2688 records from 5 relevant database searches . After eThe characteristics of the included studies are summarized in This review encompassed 1257 participants -32; 498 2.The majority of participants had NAFLD, regardless of whether they were overweight, obese, or diabetic. However, Lim et al  only recThe characteristics of the interventions are presented in All studies measured changes in body weight, BMI, and liver enzymes ,27-32. FThis review classified the intervention features of lifestyle modification of NAFLD into 8 components: initial assessment, information on NAFLD, education related to disease or healthy lifestyle, goal setting, counseling of diet and physical activity, reminders for self-management behaviors, feedback, and interactions as social support . Each inThe intervention frequency varied according to the contents of each study. Most messages for facilitating lifestyle behaviors or motivational information were sent daily ,30, and Out of 7 studies, 4 were included in the meta-analysis -30. RegaP=.05), with a moderate level of heterogeneity (I2=72%). There was a statistically significant difference in BMI  between the intervention and control groups, as shown in the forest plot, with a moderate level of heterogeneity . As shown in  I2=72%; . SignifiThe details of the 4 RCTs’ risk of bias are summarized in This systematic review summarized the results for the effectiveness of eHealth interventions for lifestyle modifications in body weight, BMI, ALT, and AST among adult patients with NAFLD. All the interventions were published between 2016 and 2022. This shows that eHealth technology is not yet widely applied in NAFLD treatment interventions. This study is therefore significant, as it is the first review to identify the effect of eHealth-enabled interventions for supporting sustainable NAFLD management.Interventions using eHealth for NAFLD were beneficial at achieving weight loss and effective in improving BMI according to the findings of our systematic review and meta-analysis. The NAFLD guideline states that weight loss is the primary goal in the treatment of NAFLD, which leads to reduced liver fat . AlthougThe eHealth interventions to improve BMI were effective when conducted for 3 months or longer and ranged in duration from 3 to 24 months. However, in a meta-analysis of BMI, 2 of the 3 studies that were performed for 6 months were more effective than those that were performed for 24 months. Additionally, the frequency of contact using the intervention component delivered to participants varied from daily ,30 to 1-Lifestyle modification approaches use various intervention contents, such as assessment of participants’ lifestyle behaviors, information, education, goal setting, counseling, and feedback. In particular, most of the interventions included in the meta-analysis to examine the effectiveness of outcomes consisted of the assessment of health conditions, information on disease treatment or healthy behavior, goal setting of the behavior, and feedback on performance or self-monitoring. The effectiveness of these strategies in promoting changes in unhealthy lifestyle behaviors has been presented in previous eHealth interventions on weight management in other diseases ,37. It iFurthermore, the results of this review show that lifestyle modification interventions enabled eHealth to significantly improve biomarkers related to liver health. Some studies evaluating improvements in liver histology resulting from weight loss through lifestyle modification have reported significantly reduced ALT and AST -42, and Lifestyle modification, in the absence of pharmacological treatment, is important for patients with NAFLD to treat their health condition; it should be continued with a multidisciplinary team approach including health care professionals, dietitians, and exercise experts to motivate and guide these patients. This study highlights that eHealth technology-based lifestyle modifications may help not only with weight loss but also in improving unhealthy liver conditions through the control of liver enzymes.This study included clinical trials of lifestyle modification programs delivered by eHealth for the self-management of NAFLD and the first systematic review that analyzed the characteristics and effects using meta-analysis. Additionally, the results add to the existing knowledge on the improvement of NAFLD management.This systematic review and meta-analysis have some limitations. First, the number of analyzed studies was small. This may reduce the generalizability of the findings and make it difficult to recommend the use of eHealth in clinical trials. Second, self-reporting of compliance related to diet and exercise potentially has a probability of recall and social desirability bias. This bias would affect the effectiveness of the outcomes. Lastly, the duration of the included studies ranges from 3 to 24 months. Thus, more evidence is needed to confirm the duration to improve outcomes of eHealth interventions in this population.This systematic review and meta-analysis aimed to identify and evaluate the effectiveness of eHealth interventions on lifestyle modifications in patients with NAFLD. The results demonstrated that an eHealth intervention was effective in improving body weight, BMI, and ALT and AST levels among these participants. In conclusion, eHealth technology has proven to be a potentially useful intervention strategy for modification of diet and exercise habits and will be an important facilitator to improve self-management skills for patients with NAFLD.


Monoaminergic neurotransmitter systems play a central role in neuronal function and behaviour. Dysregulation of these systems gives rise to neuropsychiatric and neurodegenerative disorders with high prevalence and societal burden, collectively termed monoamine neurotransmitter disorders (MNDs). Despite extensive research, the transcriptional regulation of monoaminergic neurotransmitter systems is not fully explored. Interestingly, certain drugs that act on these systems have been shown to modulate central levels of the transcription factor AP-2 beta . AP-2β regulates multiple key genes within these systems and thereby its levels correlate with monoamine neurotransmitters measures; yet, its signalling pathways are not well understood. Moreover, although dysregulation of TFAP2Β has been associated with MNDs, the underlying mechanisms for these associations remain elusive. In this context, this review addresses AP-2β, considering its basic structural aspects, regulation and signalling pathways in the controlling of monoaminergic neurotransmitter systems, and possible mechanisms underpinning associated MNDS. It also underscores the significance of AP-2β as a potential diagnostic biomarker and its potential and limitations as a therapeutic target for specific MNDs as well as possible pharmaceutical interventions for targeting it. In essence, this review emphasizes the role of AP-2β as a key regulator of the monoaminergic neurotransmitter systems and its importance for understanding the pathogenesis and improving the management of MNDs. The monoaminergic neurotransmitter systems, including dopaminergic (DA), adrenergic, noradrenergic (NA) and serotonergic (5-HT) circuitries, regulate a wide range of neurological functions. Dysregulation of these systems is associated with a variety of neuropsychiatric, neurodevelopmental and neurodegenerative disorders, which forms the basis for the so-called monoamine neurotransmitter disorders (MNDs) , 2, inclO-methyltransferase inhibitors (COMTIs) are associated with serious adverse and off-target effects as well as interactions with foods and other drugs, resulting in poor patient compliance and treatment outcomes  close to the 3`-splice site of exon 2 between nucleotides 12593 and 12612 , 51. Repf TFAP2Β . The intf TFAP2Β , 29. Strf TFAP2Β , 29, 119f TFAP2Β .TFAP2Β polymorphism, rs987237, is located in intron 3 and has three different genotypes, AA, AG and GG that are associated with obesity, BMI, waist circumference and differential effects on weight loss [TFAP2Β rs987237 variant may be linked to enhanced activity of this transcription factor since it is also in complete linkage disequilibrium with intron 1 [The third ght loss –123. Stointron 1 .Overall, three TFAP2Β polymorphisms have been shown to mostly enhance its transcription activity. This in turn affects the expression of the key genes in the monoaminergic neurotransmitter systems that have putative binding sites for AP-2β.TFAP2Β polymorphisms of intron 1,2 & 3 that lead to higher expression of TFAP2Β, are associated with reward fixation, insulin resistance, T2D and adiposity, lipid droplet biogenesis as well as eating disorders [TFAP2Β rs987237 within intron 3 with obesity, BMI and waist circumference in women [Studies have shown that isorders , 124–128in women , 130. Thin women –122 and in women , 131.TFAP2Β overexpression has been shown to inhibit the expression of adiponectin and leptin but also enhance the expression of interleukin 6 (IL-6), monocyte chemoattractant protein-1 (MCP) and tumour necrosis factor-alpha  [TFAP2Β has no effect on most adipocytokines like adiponectin, leptin and IL-6 [. have shown no association of adiponectin levels to insulin resistance or to the TFAP2Β polymorphism [The mechanisms underlying AP-2β-induced insulin resistance and adiposity are rather unclear. Several studies have attributed the role of AP-2β in insulin resistance and adiposity to its regulatory effect on genes that encode adipocytokines, which have AP-2β binding sites in their promoters. For instance, F-alpha) , 132–135and IL-6 . Additiomorphism . Furthermorphism , 134. Thmorphism , 137. GiTFAP2Β contribute to eating disorder-related behaviour through catecholamine-induced orexigenic signals [PNMT [PNMT has been associated with elevated epinephrine and reduced circulating leptin levels [Many research studies have revealed that polymorphisms or overexpression of  signals , 79. In  signals , 25, and signals , 139. Mo signals , 25, bot signals –145. Furls [PNMT , 86–88, n levels , both ofn levels , 146.TFAP2Β with obesity-related disorders might also be linked to its repression effect on 5-HTT. First, 5-HTT re-uptakes 5-HT from the synaptic cleft into the presynaptic neurons. As we mentioned previously, AP-2β represses the expression of 5-HTT [ 5-HTT gene promotor methylation is associated with reward and eating behaviour as well as human obesity [5-HTT, by hypermethylation, in humans is associated with a high prevalence of obesity [.The association of of 5-HTT , 20, 101 obesity  and supp obesity .TFAP2Β and KCTD15, an obesity-linked gene, is also likely to positively contribute to AP-2β-induced obesity and metabolic dysfunction. In this regard, our group has recently shown that in Drosophila, the homologue of KCTD15, Twz, genetically interacts with Drosophila TfAP-2 to regulate octopaminergic (human noradrenergic) signalling. We have further demonstrated that mouse TFAP2β and Kctd15 directly interact with Ube2i, a mouse sumoylation enzyme, to induce consummatory behaviour [. that has identified the genetic interaction between KCTD15 and TFAP2Β in individuals with eating disorders [TFAP2Β by KCTD15, a post-translational effect is initiated by E1‐3 enzymes and ATP, resulting in elevated catecholamine levels and induction of feeding behaviour [The genetic interaction between ehaviour . Strikinisorders . The samehaviour .TFAP2Β polymorphism in the intron 1, which can enhance AP-2β transcription, is associated with severe alcoholism in women [DBH) [DBH is hypomethylated and its enzyme is more active, resulting in a reduction of dopamine levels [DRD1 KO mice has been shown to reduce alcohol-seeking behaviour [That dysregulation of monoamine neurotransmitters contributes to the development of alcoholism is well known. Reports have indicated that in women . This asin women , 149–151en [DBH)  may alsoe levels , 153. Hoehaviour –157, sugehaviour .5-HTT [MAO [. In agreement with this notion, increased serotonin levels or 5-HTT KO mice have been found to trigger alcoholism [MAO-A methylation is associated with alcoholism in women [TPH [Moreover, AP-2β-enhanced serotonergic transmission may also contribute to alcohol abuse, perhaps through inhibition of 5-HTT , 20, 101HTT [MAO , 91–93. coholism –160, andin women . Furthermen [TPH , the ratmen [TPH . Increasmen [TPH .TFAP2Β intron 2 polymorphisms are associated with low anxiety [TFAP2Β intron 2 polymorphisms who are homozygous for the short TFAP2Β allele exhibited higher depression scores [ anxiety , 27, 93, anxiety . Howevern scores .This association might be ascribed to the AP-2β regulatory effect on the monoaminergic neurotransmitters in the brain. Mechanistically, AP-2β inhibits 5-HTT expression , 20, 101Since certain antidepressant/anxiolytic drugs, e.g. citalopram (SSRI) and imipramine (TCA), have been reported to modulate the levels of AP-2β in the brainstem of the rat , 38, 84,TFAP2Β has been associated with aggression in fruit flies and humans, as well as with general antisocial behaviour in humans [TFAP2Β signalling mechanisms that underlie such association have not been fully elucidated. The repressing effect of AP-2β on monoamine-degrading enzymes, such as COMT and MAO might explain such an association since several reports have revealed that lowered expression of MAO-A, or MAO-A KO mice, as well as lower activity of COMT or COMT-deficiency in mice are associated with higher aggression [DBH expression by AP-2β may also play a part in provoking high aggression. Activation of DBH enhances the conversion of dopamine to adrenaline and it has been reported that higher adrenergic signalling provokes aggression [DBH KO mice have lower levels of aggression [Antisocial behaviour in children and adolescents refers to a heterogeneous set of actions outside the norms of society, including aggression, impulsive behaviour and criminal acts, which are linked to monoaminergic neurotransmitter systems –175. TFAn humans , 81, 176gression –182. Furgression , and DBHgression , all lin5-HTT. This is because several preclinical and clinical studies have indicated that high levels of serotonin and lower expression or blocking of 5-HTT or knocking out 5-HTT in mice lead to reduced aggression outbursts and violent behaviour [. It would be interesting to investigate the association of TFAP2Β polymorphisms and their interaction with key monoamine neurotransmitter genes to aggression and antisocial behaviour.Nevertheless, the AP-2β association with higher aggression might be independent of its enhancement effect on serotonergic signalling, specifically, its repressing effect on ehaviour –188, an TFAP2Β have been suggested to play a role in resilience to AD [DBH and PNMT [TFAP2Β polymorphisms and their interactions with COMT, MAO, to AD could unveil a potential biomarker for early diagnosis and management of AD.While genetic variants of ce to AD , increasce to AD , 190. Thce to AD –193 and,ce to AD , 86–88. and PNMT –198, whiand PNMT , 86–88. and PNMT , 199–201and PNMT , 92, 93.and PNMT –204. Secand PNMT , providiand PNMT . Taken tTranscription factors serve as essential regulators of cell development, proliferation and differentiation; consequently, dysregulation of them brings about oncogenic transformation and cancers , 208. Ne. have identified TFAP2Β as a potential tumour suppressor gene in neuroblastoma [. have also indicated that low AP-2β expression results in lower noradrenergic neuronal differentiation and is thereby significantly associated with poor prognostic markers and unfavourable patient outcomes [TH and DBH [Recent studies have revealed an important role of AP-2β in the pathogenesis and progression of neuroblastoma , 210. Thoutcomes . In cont and DBH . In addi and DBH .TFAP2B in neuronal cell culture or rats’ models of MNDs and examining the levels of the key enzymes of monoaminergic neurotransmission, are warranted.Altogether, AP-2β is associated with certain MNDS, although the underlying mechanisms are not fully elucidated. As we mentioned above, the underlying mechanisms for this association might be linked to AP-2β regulatory effects on key genes of monoaminergic neurotransmitters. However, further preclinical studies investigating the molecular mechanisms underlying AP-2β associated MNDs, e.g. manipulating the expression of TFAP2Β/AP-2β, which underscores its diagnostic importance for such diseases. TFAP2Β overexpression, for instance, has been implicated not only as a protective or favourable prognostic factor in several cancers like breast, renal cell, cervical and endometrial cancers but also as a poor prognostic factor in thyroid cancer and lung adenocarcinoma [Transcription factors are currently widely used as diagnostic biomarkers for the early detection of several diseases –213. A warcinoma –225  since transcription factors lack obvious druggable pockets and the transcription process is carried out in the nucleus; therefore, the therapeutic agent should have the appropriate physicochemical properties to cross many biological barriers and reach the nucleus with sufficient concentration. Moreover, many crucial components involved in the transcription process do not have the enzymatic activity adequate for pharmaco-chemical interventions , 228.TFAP2Β/AP-2β at different levels: at transcription, post-translation or the DNA-binding site , have been successfully developed for several cancers and loss-of-function mutations to restore the overall functions of the transcription factors . DimethyTFAP2B in rat models of MNDs and examining the expression of the key monoaminergic genes can help clarify the molecular pathogenesis of the associated MNDs. Moreover, the molecular functions of AP-2β within the monoaminergic systems underline its importance as a promising biomarker for the early diagnosis of relevant MNDs. Given its effect on multiple targets within and outside monoaminergic systems, AP-2β could be considered a dirty drug target due to the odds of having adverse effects. Nevertheless, several dirty drugs that aim for multiple targets at once are clinically effective in the treatment of complex diseases [AP-2β is a central regulator of monoamine neurotransmitters and its dysregulation is associated with MNDs. The underlying mechanisms for its associated MNDs could be linked to its regulatory role in monoaminergic transmission, yet more studies are recommended. For example, knockout/overexpression of diseases , 241, wh


Genomic features, including tumor mutation burden (TMB), microsatellite instability (MSI), and somatic copy number alteration (SCNA), had been demonstrated to be involved with the tumor microenvironment (TME) and outcome of gastric cancer (GC). We obtained profiles of TMB, MSI, and SCNA by processing 405 GC data from The Cancer Genome Atlas (TCGA) and then conducted a comprehensive analysis though “iClusterPlus.” A total of two subgroups were generated, with distinguished prognosis, somatic mutation burden, copy number changes, and immune landscape. We revealed that Cluster1 was marked by a better prognosis, accompanied by higher TMB, MSIsensor score, TMEscore, and lower SCNA burden. Based on these clusters, we screened 196 differentially expressed genes (DEGs), which were subsequently projected into univariate Cox survival analysis. We constructed a 9-gene immune risk score (IRS) model using LASSO-penalized logistic regression. Moreover, the prognostic prediction of IRS was verified by receiver operating characteristic (ROC) curve analysis and nomogram plot. Another independent Gene Expression Omnibus (GEO) contained specimens from 109 GC patients was designed as an external validation. Our works suggested that the 9‐gene‐signature prediction model, which was derived from TMB, MSI, and SCNA, was a promising predictive tool for clinical outcomes in GC patients. This novel methodology may help clinicians uncover the underlying mechanisms and guide future treatment strategies. Immune checkpoint blockade (ICB) therapy targeting programmed cell death protein 1 (PD-1) and cytotoxic T lymphocyte antigen 4 was tolerated with manageable toxicities and promising antitumor effect in patients with GC . HoweverCancer genomic characteristics had a high profile due to their key role in ICB resistance and their potential in biomarker prediction. Currently, several biomarkers had been used to predict ICB responses despite some limitations. For example, tumor mutation burden (TMB) high was well documented to contribute to therapeutic response to ICB, especially in patients with melanoma and non-small cell lung cancer . TMB higRecently, the integration of multiple omics profiles with high-throughput molecular analysis had been a major focus for the discovery of multiple cancer subgroups. Deep learning approaches allowed for a systematic understanding of genomic, proteomic, biochemical, metabolic, and epigenetic processes. The most commonly used integration tools include “mixOmics,” “tRanslatome,” “R.JIVE,” and “iClusterPlus”. First, “mixOmics” was a powerful framework with four kinds of datasets  . Then, aIn the present study, integrative clustering of three genomic datasets including TMB, SCNA, and MSI were used to investigate subgroups of GC through “iClusterPlus” software. We further estimated the TME infiltration patterns of stomach adenocarcinoma (STAD) from TCGA and GEO data and systematically analyzed the clusters’ relationship with genomic characteristics and clinical features in GC. We incorporated the TME infiltration evaluation into an immune risk score (IRS) to predict ICB therapeutic efficacy and survival outcomes from tumor genomic data. Depicting the immune landscape features of GC, more importantly, contributes to interpret the immunotherapy response of GC and provide new strategies for cancer treatment.http://cbioportal.org/) and UCSC Xena (http://xena.ucsc.edu/) websites. Finally, 405 patients with complete data were screened for subsequent analysis. The clinical characteristics of involved patients are displayed in TCGA-STAD gene expression data (n = 440), mutation annotation format (MAF) (n = 440), somatic copy number data (n = 405), and clinical data of the corresponding patient (n = 444) were obtained from cBioPortal  and normalized by fragment length after log2 transformation.We defined mTMB as the total number of unique genes with mutations in each patient. Only seven types of mutations in this gene were considered as mTMB event: Frame_Shift_Del, Translation_Start_Site, Frame_Shift_Ins, Splice_Site, Non-stop_Mutation, Non-sense_Mutation, and Missense_Mutation. After removing no functional relevance mutations, we merged the MAF data of TCGA-STAD. Then, the low-frequency mutated genes were filtered through a cutoff value . As a result, we extracted 1932 high-frequency mutated genes in 405 patients, and the binary description matrix of mTMB feature was used with subsequent calls.TMB burden was computed by the total number of somatic mutations per Mb in each sample. Since 38 Mb is usually taken in terms of the length of human exons, the TMB burden was equal to the total mutation frequency/38 .MSIsensor-pro algorithm of the Linux operating system was used to investigate the MSI traits of TCGA-STAD data at the microsatellite transcription level . We seleWe constructed a comprehensive data of 2,024 genome variant characteristics, including 54 copy number gains, 37 copy number losses, 1 MSI, and 1,932 genes. Subsequently, we characterized the SCNA, mTMB, and MSI traits in a binary form to delineate whether corresponding genomic alteration occurred in each patient. In detail, 1 indicated the presence of genomic changes while 0 indicated the absence of genomic changes in this data, which formed the sample matrix with three binary signatures.In our genomic variation profiles description matrix, the columns represent various samples, while rows represent the corresponding genomic signatures. In total, 405 valid samples were classified by “iClusterPlus,” a comprehensive clustering method in the R package . With deMutation analysis was performed both in Cluster1 and Cluster2 based on the “maftools” package in R. The default arguments were set to analyze the MAF of the TCGA-STAD dataset in each cluster. The mutation results were directly visualized by “oncoplot” function of the “maftools” R package. The analysis of CNAs was performed with GISTIC2 on the Linux system. The specific “-conf” parameter was set to 0.95, and top 10 significant copy number alteration areas were displayed in both clusters with “gisticOncoPlot” function. The G-score across all chromosomes was visualized based on the frequency of the CNAs and the average amplitude in the log ratio.The cancer immune infiltration profiles in the two clusters were compared based on the gene expression data of TCGA-STAD. The proportion of various immune cells and proportion of each sample were computed through “CIBERSORT” software under the default parameters. Differences of the immune cell landscape between Cluster1 and Cluster2 were investigated by “boxplot”. Additionally, the well-established “TMEscore” was used to analyze the difference in immune efficacy between the two clusters .p < 0.05.Based on the TCGA-STAD gene expression data (counts), we used the “DESeq2” package in R to screen out the DEGs between the two clusters. The significance threshold for DEGs was set to abs (log2FC) > 1 and p < 0.05), we carried out the hub genes as independent prognostic factors. The “glmnet” package was used to perform LASSO-penalized regression on the samples and corresponding DEGs. The arguments used in LASSO-penalized regression are alpha = 1, nlambda = 100, and p < 0.05 was considered as the significant threshold. By “coef” function, we got the Y-intercept and hazard rate (HR) score of each gene. Then, HR score, Y-intercept, and corresponding hub gene expression profiles were used to measure the IRS value of all samples.The normalized expression data of the DEGs were subsequently converted into binary fashion by comparing the median value of each gene in all samples. After combining with clinical data, DEGs were further selected in univariate cox analysis using “coxph” function of the “survival” package in R. With default parameters and significance (https://www.r-project.org/), and p-value < 0.01 indicated a statistically significant threshold.The unpaired Student’s t-test was developed to estimate the comparison between two normally distributed variables. In contrast, non-normally distributed variables were measured by the Wilcoxon rank-sum test. In order to compare more than two groups, Kruskal–Wallis tests and ANOVA were used as the non-parametric method and the parametric method, respectively. Two-sided Fisher’s exact tests were used to analyze contingency data. The Kaplan–Meier method was used to offer a visual representation of predicted survival curves for each cluster data with “ggplot2” and “survminer” packages. Area under the curve (AUC), sensitivity, and specificity were depicted by the “pROC” package. All statistical analyses in this study were performed on R version 4.0.4 , macrophages M1 (p < 0.0001), neutrophils (p = 0.0002), NK cells active (p = 0.0078), NK cells resting (p = 0.0008), t cells CD4 memory active (p < 0.0001), and T cells CD8 (p < 0.0001) was markedly higher in Cluster1 than that in Cluster2. On the other side, compared with Cluster1, the infiltration of B cells naïve (p = 0.0002), mast cells resting (p = 0.0084), t cells CD4 memory resting (p < 0.0001), t cells CD4 naïve (p = 0.0141), and t cells gamma delta (p = 0.0165) were much extensive in Cluster2. Moreover, we selected seven immune cell types which showed high infiltration in more than half of the samples , in which SNORA12 were revealed to be the protective factors, while TF, SLC13A5, ASF1B, RSPO4, GRIN3B, ANXA8, PPBP, and ALOX15 were the risk factors . AUC was computed to test the discriminatory powers over 1-year, 3-year, and 5-year outcome . Therefore, we developed a nomogram predicting the probability of a patient who will survive GC for 1, 3, and 5 years. Our work provided a new approach to accelerate accurate immunotherapy, which may optimize combination therapy strategies.


The extent of lung damage was similar across the upper, middle, and lower fields. Furthermore, upon comparing the extent of lung damage based on the number of days after onset, a significant difference was found between the severe pneumonia group (SPG) with intubation or those who died and non-severe pneumonia group (NSPG) ≥3 days after onset, with aggravation observed when ≥14.5% of the lungs exhibited damage at 3–5 days  and when ≥20.1% of the lungs exhibited damage at 6–8 days . Patients with aggravation suddenly developed hypoxemia after 7 days from the onset; however, chest CT scans obtained in the paucisymptomatic phase without hypoxemia indicated that subsequent aggravation could be predicted based on the degree of lung damage. Furthermore, in subjects aged ≥65 years, a significant difference between the SPG and NSPG was observed in the extent of lung damage early beginning from 3 days after onset, and it was found that the degree of lung damage could serve as a predictor of aggravation. Therefore, to predict and improve prognosis through rapid and appropriate management, evaluating patients with factors indicating poor prognosis using chest CT is essential.Presently, coronavirus disease-19 (COVID-19) is spreading worldwide without an effective treatment method. For COVID-19, which is often asymptomatic, it is essential to adopt a method that does not cause aggravation, as well as a method to prevent infection. Whether aggravation can be predicted by analyzing the extent of lung damage on chest computed tomography (CT) scans was examined. The extent of lung damage on pre-intubation chest CT scans of 277 patients with COVID-19 was assessed. It was observed that aggravation occurred when the CT scan showed extensive damage associated with ground-glass opacification and/or consolidation ( Coronavirus disease-19 (COVID-19) that broke out in 2019 in Wuhan, Hubei Province, China, has spread worldwide . Under sTo this end, it is crucial to accurately understand the state of COVID-19 and to perform early triage of patients whose condition will aggravate. COVID-19 is broadly divided into the asymptomatic phase, in which infection has occurred with no obvious symptoms; the paucisymptomatic phase, in which flu-like symptoms and symptoms such as taste disorder are exhibited but oxygenation is maintained; and severe pneumonia phases, in which respiratory distress progresses, causing hypoxemia.If aggravation can be predicted at an early stage before hypoxemia sets in, that is, in the asymptomatic and paucisymptomatic phases, then it would be possible to administer unverified but promising treatments, such as steroid inhalation and antibody cocktail therapy.Among patients with COVID-19, approximately 3% develop severe pneumonia and die . PreviouHowever, in reality, only some patients with these factors demonstrate disease aggravation, whereas others do not; thus, these factors are inadequate predictors of aggravation. Additionally, laboratory test values, including lymphocyte count, ferritin level, and C-reactive protein (CRP) level are considered factors that correlate with aggravation , 8. It wChest CT scans of patients with pneumonia demonstrate ground-glass opacification (GGO), which is also observed in other viral illnesses. Thus, chest CT scans were also considered as inadequate predictors of aggravation because of the inability to differentiate the causative illnesses. Additionally, in patients with COVID-19, consolidation is observed only in some areas, and the significance of consolidation is unclear.Therefore, since our institution accepts patients with moderate to severe pneumonia by COVID-19, we investigated what predictors of severe disease were present in patients who consequently transitioned from moderate to severe disease. Particularly, whether chest CT scans can predict the aggravation of pneumonia was examined. Additionally, if aggravation can be predicted, how soon it can be predicted after the appearance of symptoms was examined. Moreover, whether aggravation can be predicted based on the extent of GGO and consolidation was examined qualitatively and quantitatively.This was a retrospective cohort study. All patients with COVID-19 were diagnosed based on a polymerase chain reaction test on a throat swab. The subject sample included 277 patients with COVID-19 admitted to our hospital from February 16, 2020 to March 9, 2021, and chest CT scans of these patients taken before Ventilator management were evaluated. The clinical course analysis included chest CT scans taken within 14 days of onset. The day of onset was defined as the day of appearance of clinical symptoms [fever or respiratory symptoms (coughing or runny nose) and taste disturbance]. Pneumonia was confirmed based on relevant chest CT scan findings. The severe pneumonia group (SPG) included patients with intubation and conventional ventilator therapy or death. Additionally, patients who were excluded from the SPG were included in the non-severe pneumonia group (NSPG). Patients who were intubated for conditions other than pneumonia were excluded from the evaluations. In many cases, for isolation management, the informed consent was taken orally. This study was conducted with the approval of the institutional review board .The age and gender of the subjects as well as the presence or absence of underlying illnesses in them were evaluated. Blood tests conducted from the day of onset until 14 days thereafter as well as those conducted before intubation were included in evaluations. Tests conducted several times were evaluated using the minimum and maximum values.Additionally, the white blood cell, lymphocyte, and platelet counts; red cell distribution width; and CRP, ferritin, D-dimer, and sialylated carbohydrate antigen KL-6 levels were evaluated.The history of use of inhaled steroids including Ciclesonide, non-inhaled steroids, favipiravir, and remdesivir was evaluated as factors affecting the SPG. These evaluations included influential factors found after onset until 48 hours before the day of intubation. The chest CT scans of patients aged ≥65 years were evaluated as age was also considered as one of the factors of aggravation.Chest CT scans were independently interpreted and evaluated by two radiologists. The chest CT scans were interpreted separately in terms of the upper, middle, and lower field sections of the left and right sides. The upper field section included slices up to the aortic arch, the middle field section included slices up to the carina, and the lower field section included slices up to 1-cm above the diaphragm. In each of these sections, 1–2 slices were interpreted, the extent of consolidation and GGO was measured, and the mean was calculated. Based on previous reports, the ratios of the upper, middle, and lower lung regions were estimated to be 1:1.6:1.3.  Chest CT0, no involvement; 1, < 5% involvement; 2, 5–25% involvement; 3, 26–50% involvement; 4, 51–75% involvement; and 5, > 75% involvement. The CT severity score was the sum of each individual lobar score (0 to 25).Data were analyzed using the Mann–Whitney U test, Fisher’s exact test, and regression analysis using Graph Pad Prism 6 (GraphPad Software Inc.).A receiver operating characteristic (ROC) curve was created using JMP® 15 , and the cut-off values for sensitivity and specificity were calculated.Among the 277 patients, pre-intubation chest CT scans of 65 patients were unavailable; thus, they were excluded from this study. Intubation was performed for myocardial infarction in 3 patients, pulmonary embolism in 4 patients, and gastrointestinal perforation in 1 patient, and thus these patients were excluded from this study.As a result, 204 patients were included, with 84 patients in the SPG and 120 patients in the NSPG. In the analysis of the clinical course after the onset day, nine patients were excluded due to chest CT scans being taken on day 15 or later after onset, and 6 patients were excluded because the onset day was unknown. Therefore, ultimately, a total of 189 patients were included, of which 76 were included in the SPG and 113 in the NSPG .The patient background is indicated in Upon comparing the 84 patients in the SPG and the 120 patients in the NSPG, a significant difference was found in factors that are generally considered to cause aggravation, and the trends in such factors were comparable .Among the aggravation factors for existing reports, there was an insignificant difference between the SPG and NSPG in terms of age and the presence or absence of underlying illnesses.p = 0.009) in terms of gender, with more men in the SPG. Consistent with a previous report https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  8 Apr 2022Response to Reviewers' comments:Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: SummaryGoal of this paper is trying to identify predictors for COVID-19 aggravation in a retrospective clinical analysis. Chest CT scan done by 15 days after disease onset is the key target for this study. 189 candidates were enrolled and divided into SPG(n=76) and NSPG(n=113) groups. It is concluded that the degree of lung damage detected by chest CT scanning in the early stage (3 days after onset) could serve as a predictor for disease aggravation.⇒We thank the reviewer for the advice. Major Issues(1)Line 108 : “The severe pneumonia group (SPG) included patients with intubation or respiratory management and those who died.”----- 1. The definition of “respiratory management” is not precise enough. Does it include oxygen therapy, conventional ventilator therapy, NIPPV, HFNC, prone therapy and/or ECMO? ⇒The definition of "respiratory management" is ventilator management.Therefore, the definition of "severe pneumonia" in this study is ventilator management or death.It is described in lines 143-144.The severe pneumonia group (SPG) included patients with intubation and conventional ventilator therapy or death.2. In addition to severe pneumonia with acute respiratory failure, COVID-19 victims may also die of sepsis, cardiac dysfunction, thrombosis or other comorbidities. These cases will be recruited into the SPG group despite their lungs are less damaged, and that will distort the results of chest CT analysis.⇒ "Myocardial infarction" and "pulmonary embolism" are excluded in this study.It is described in lines 145-146.Patients who were intubated for conditions other than pneumonia were excluded from the evaluations.It is described in lines 186-188.Intubation was performed for myocardial infarction in 3 patients, pulmonary embolism in 4 patients, and gastrointestinal perforation in 1 patient, and thus these patients were excluded from this study.(2)Line 129 : “Analysis of chest CT scans”-----In this study the methodology of chest CT analysis adopted a scoring system from Muller NL (ref 10) which was specifically for pulmonary sarcoid analysis in 1989. The details of estimation method was not given in this paper. Nevertheless, new expert consensus and chest CT scoring system specifically for COVID -19 infections were available in 2020, which will be more specific and updated for your analysis.Ref:# Marco Francone, Franco Iafrate, Giorgio Maria Masci, et al. Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis. European Radiology 2020;30(12):6808-6817.# Feng Pan, Tianhe Ye, Peng Sun, et al. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology 2020; 295:715–721.# Simpson S, Kay FU, Abbara S, Bhalla S, Chung JH, Chung M, Henry TS, Kanne JP, Kligerman S, Ko JP, Litt H. Radiological Society of North America expert consensus statement on reporting chest CT findings related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA - secondary publication. J Thorac Imaging 2020;35:219-27.# Geoffrey D. Rubin, Christopher J. Ryerson, Linda B. Haramati, et al. The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society. Radiology 2020; 296:172–180.⇒The CT severity scoring of 2020 is had scoring of for each lobe. Therefore the right middle lobe of approximately 10% of total lung is actually estimated as 20% by CT severity scoring. As CT severity scoring was not exact for an area, we chose a standard method.However, in consideration of the need of the on-site physician who used CT severity scoring, we wrote score jointly.It is described in lines 171-175. CT severity scoring [11] was calculated per each of the 5 lobes considering the extent of involvement, as follows:0, no involvement; 1, < 5% involvement; 2, 5-25% involvement; 3, 26-50% involvement; 4, 51-75% involvement; and 5, > 75% involvement. The CT severity score was the sum of each individual lobar score (0 to 25).It is described in lines 226-227.The ROC curve for CT severity score demonstrated a sensitivity of 83.3%, specificity of 62.8%, and cut-off value of 9.It is described in lines 287-289.In the ROC curve for CT severity score, the sensitivity, specificity, and cut-off values were 70.6%, 75.0%, and 6 in the T2 period; 76.4%, 67.1%, and 9 in the T3 period; and 90.9%, 83.3%, and 10 in the T4 period.(3)Line 202-224 : “Analysis according to site”, “…..in terms of the area with damage combining GGO and consolidation in the upper, middle, and lower lobes .”-----According to your methodology the so called “the upper, middle, and lower lobes” in this study actually are not the same the terminology of upper, middle, and lower lobes in lung anatomy. Readers will get confused if your “lobe” is actually not a lobe, but a region in the lung.⇒The expression "lobe" and “site” has been changed to "regions".Minor IssuesLine 225-232 : “Clinical course analysis” “Figure 4A”----- Simple linear regressions were conducted separately for both SPG and NSPG for different CT lesions. Please specif the y Y and X variables in the liner regression equation.⇒In Figure 4a, we added Y = 2.822 × X + 20.18 as SPG and Y = 1.353 × X + 11.93 as NSPG.In Figure 5b, we added Y = 2.750 × X + 23.27 as SPG and Y = 1.139 × X + 13.51 as NSPGOther CommentLine 73: “several pneumonia phases”----- “severe pneumonia” or “several pneumonia” ?⇒The expression " several pneumonia" has been changed to " severe pneumonia” in line 107Line 93 :“As our institution receives patients with moderate to severe pneumonia caused by COVID-19, factors that can predict aggravation from moderate to severe illness in patients who demonstrate the same were examined.”----- “who demonstrate the same” ???⇒In 126, we added “Therefore, since our institution accepts patients with moderate to severe pneumonia by COVID-19, we investigated what predictors of severe disease were present in patients who consequently transitioned from moderate to severe disease.”Line 98 : “was qualitatively examined.”----- qualitatively or quantitatively or semi-quantitatively examined in this study?In line132, we changed as “was examined qualitatively and quantitatively.”migi Line 123 : “KL6 levels”----- KL6 is not a popular abbreviation. Please provide full name in front of abbreviation.⇒In line156, we changed as “sialylated carbohydrate antigen KL-6”Line 124: “and the use of non-inhaled steroids, including favipiravir and remdesivir”----- Favipiravir and remdesivir are actually anti-viral agents, not non-inhaled steroids.Inlind157, we added “The history of use of inhaled steroids including Ciclesonide, non-inhaled steroids, favipiravir, and remdesivir was evaluated as factors affecting the severe pneumonia group.”Line 290 310: “It has been highlighted that COVID-19 is an asymptomatic infection”----- ⇒In Line 349, we changed to “It has been highlighted that COVID-19　is often asymptomatic and is difficult to control.”Reviewer #2: This is a retrospective study. Although 277 COVID-19 patients were accessed initially, 65 patients were excluded because they had no computerized tomography, 8 patients were excluded because they had no pneumonia presented at chest CT scans. Fifteen patients were excluded due to clinical courses were unclear, so only 189 patients were included for course analysis. Given the study designs are not prospective cohort setting, there are some concerns for the interpretation of the results:⇒We thank the reviewer for the advice. Major concern 1: “Age” is an aggravation factors for most of the critical illness, according to existing reports, there was an insignificant difference between the SPG and NSPG in terms of age. The authors should address further analysis of the interaction between age factor and the presence or absence of underlying illnesses.Figure 5 separately analyzes the individuals who were more than 65 years old, with significant differences. The readers must also request to see this comparison in younger group.⇒In line 220, we added “Patients 65 years or older were more frequent of associated disease than younger patients (90.3% vs 65.2%).”In line 299, we added “As data under 65 years old, there was not a significant difference observed in the chest CT scans in the SPG and NSPG,”In line 304, we added “The data under 65 years old did not significantly have linear regression in the SPG and NSPG.,”Major concern 2:There should be other chest computerized tomography image findings, such as interstitial infiltration, septal thickening, cavitating lung lesions, or pneumothorax, etc. The author should try their bet to evaluate possible prognosticator among them.⇒ These findings were rare and did not make a significant difference. Therefore the extent of consolidation and GGO was measured, and the mean was calculated.Major concern 3:Compared to COVID-19 patients with NSPG, those with SPG overall had higher GGO and consolidation scores; however, their values were overlapping seriously. This makes the results only provide us some insight in realizing pneumonia degree and extension correlate with clinical severity. It will be more practical to develop a chest computerized tomography scoring system to predict clinal outcomes. Please refer to the published paper “Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19” at Radiology: Cardiothoracic Imaging Volume 2: Number 2, 2020.⇒The CT severity scoring of 2020 is had scoring of for each lobe. Therefore the right middle lobe of approximately 10% of total lung is actually estimated as 20% by CT severity scoring. As CT severity scoring was not exact for an area, we chose a standard method.However, in consideration of the need of the on-site physician who used CT severity scoring, we wrote score jointly.It is described in lines 171-175. CT severity scoring [11] was calculated per each of the 5 lobes considering the extent of involvement, as follows:0, no involvement; 1, < 5% involvement; 2, 5-25% involvement; 3, 26-50% involvement; 4, 51-75% involvement; and 5, > 75% involvement. The CT severity score was the sum of each individual lobar score (0 to 25).It is described in lines 226-227.The ROC curve for CT severity score demonstrated a sensitivity of 83.3%, specificity of 62.8%, and cut-off value of 9.It is described in lines 287-289.In the ROC curve for CT severity score, the sensitivity, specificity, and cut-off values were 70.6%, 75.0%, and 6 in the T2 period; 76.4%, 67.1%, and 9 in the T3 period; and 90.9%, 83.3%, and 10 in the T4 period.Minor concern 1:Since the 65 patients without CT scans and the 8 patients without pneumonia presented at chest CT scans were excluded, the title should be verified as “The aggravation of coronavirus disease-19 pneumonia in chest computed tomography scans”.⇒We changed the title to “The aggravation of coronavirus disease-19 pneumonia in chest computed tomography scans”Minor concern 2:Figure 2 to Figure 5 should annotate the longitudinal axis meaning to indicate what kind of percentage. Figure 2A lacks horizontal bar to indicate mean values.⇒ We added “Lung damage volume (%)” in Figure 2 to Figure 5AttachmentResponse to reviewers.docxSubmitted filename: Click here for additional data file. 2 May 2022 <div> PONE-D-21-37765R1 </div>  <div> Predicting the aggravation of coronavirus disease-19 pneumonia using chest computed tomography scans </div>  <div> PLOS ONEDear Dr. Seido Ooka,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Please submit your revised manuscript by May 31, 2022. 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Is the manuscript presented in an intelligible fashion and written in standard English? </font> PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: Yes********** <font color="black"> 6. Review Comments to the Author </font> Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: CommentsMinor Issues:Line 205 Table 1The superscript characters of “a” and “b” represent “number” and “standard deviation” respectively in Table 1. However, there are already abbreviations of “n” and “SD” to represent “number” and “standard deviation” in the same table. The superscript characters of “a” and “b” are superfluous.Line 215 Table 2The superscript characters of “a”, “b” and “c” represent “not significant” and “number” and “standard deviation” respectively in Table 2. However, there are already abbreviations of “n.s.”, “n” and “SD” in the table. The superscript characters of “a”, “b” and “c” are superfluous.Line 251“middlefield” →“middle field”Line 268“The time point when a significant difference would appear in findings in CT scans conducted after the day of onset was examined.” →“The time point (X) when a significant difference would appear in findings in CT scans conducted after the day of onset was examined. ”Line 269“ In the area with damage combining GGO and consolidation,” →“In the area with damage(Y) combining GGO and consolidation,”********** <font color="black"> 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). 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Please note that Supporting Information files do not need this step. </div> While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  28 Sep 2022Thank you very much for your reviewing. We totally agree to your suggestions and re-vised the text. Revised portions were colored red. We believe that this revision made the text better than before. Please, consider my article for publication in your journal.Reviewers' comments:Reviewer #1:Line 205 Table 1The superscript characters of “a” and “b” represent “number” and “standard deviation” respectively in Table 1. However, there are already abbreviations of “n” and “SD” to represent “number” and “standard deviation” in the same table. The superscript charac-ters of “a” and “b” are superfluous.⇒We thank the reviewer for the advice. I deleted the superscript characters of "a" and "b"Line 215 Table 2The superscript characters of “a”, “b” and “c” represent “not significant” and “number” and “standard deviation” respectively in Table 2. However, there are already abbrevia-tions of “n.s.”, “n” and “SD” in the table. The superscript characters of “a”, “b” and “c” are superfluous.⇒We thank the reviewer for the advice. I deleted the superscript characters of "a" and "b"Line 251“middlefield” →“middle field”⇒We thank the reviewer for the advice. I corrected it according to your advice.Line 268“The time point when a significant difference would appear in findings in CT scans con-ducted after the day of onset was examined.” →“The time point (X) when a significant difference would appear in findings in CT scans conducted after the day of onset was examined. ”⇒We thank the reviewer for the advice. I corrected it according to your advice.Line 269“ In the area with damage combining GGO and consolidation,” →“In the area with dam-age(Y) combining GGO and consolidation,”⇒We thank the reviewer for the advice. I corrected it according to your advice.AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 13 Oct 2022Predicting the aggravation of coronavirus disease-19 pneumonia using chest computed tomography scansPONE-D-21-37765R2Dear Dr. Seido Ooka,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. 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Zea mays L.]. The compact plant phenotype, with a smaller leaf angle, is suited for high-density planting and thus for increasing crop yields. Here, we studied the ethyl methane sulfonate (EMS)-induced mutant bhlh112. Leaf angle and plant height were significantly decreased in bhlh112 compared to the wild-type plants. After treatment of seedlings with exogenous IAA and ABA respectively, under the optimal concentration of exogenous hormones, the variation of leaf angle of the mutant was more obvious than that of the wild-type, which indicated that the mutant was more sensitive to exogenous hormones. Transcriptome analysis showed that the ZmbHLH112 gene was related to the biosynthesis of auxin and brassinosteroids, and involved in the activation of genes related to the auxin and brassinosteroid signal pathways as well as cell elongation. Among the GO enrichment terms, we found many differentially expressed genes (DEGs) enriched in the cell membrane and ribosomal biosynthesis, hormone biosynthesis and signaling pathways, and flavonoid biosynthesis, which could influence cell growth and the level of endogenous hormones affecting leaf angle. Therefore, ZmbHLH112 might regulate leaf angle development through the auxin signaling and the brassinosteroid biosynthesis pathways. 12 genes related to the development of leaf were screened by WGCNA; In GO enrichment and KEGG pathways, the genes were mainly enriched in rRNA binding, ribosome biogenesis, Structural constituent of ribosome; Arabidopsis ribosome RNA methyltransferase CMAL is involved in plant development, likely by modulating auxin derived signaling pathways; The free 60s ribosomes and polysomes in the functional defective mutant rice minute-like1 (rml1) were significantly reduced, resulting in plant phenotypic diminution, narrow leaves, and growth retardation; Hence, ribosomal subunits may play an important role in leaf development. These results provide a foundation for further elucidation of the molecular mechanism of the regulation of leaf angle in maize.Leaf angle is an important agronomic trait in maize [ Zea mays L.) is one of the three largest food crops grown in China (Maize (in China . It is ain China . Since 2in China .Oryza sativa L.) auxin response factors (ARFs), OsARF6 and OsARF17, which are highly expressed in lamina joint tissues, control flag leaf angle in response to auxin , gibberellins (GAs), and brassinosteroids (BRs) are involved in regulating the leaf inclination. IAA is critical to the development of leaf morphology in maize, and interacts with light  or ethylene in regulation of leaf declination . In addito auxin . And ricto auxin . In addito auxin .Basic helix-loop-helix (bHLH) transcription factor (TF) is a major regulatory factor, which is one of the largest TF family, and widely distributed among eukaryotic kingdoms. The bHLH domain generally contains approximately 60 amino acids and possesses two functionally distinct regions: a basic region and a helix-loop-helix (HLH) region . The basbHLH genes in plants have also found to be involved in the biosynthesis pathways of anthocyanins  mutagenesis of wild-type B73, which displays a relatively small leaf angle. Maize mutant bhlh112 and wild-type B73 were analyzed using RNA-Seq sequencing. Our findings will provide useful information for further studies in maize plant type breeding to clarify the functions of bHLH TF in diverse growth and development processes.At present, the regulation functions of bHLH TF of leaf angle are widely studied in rice, but important questions remain. In this study, we obtained the bhlh112 (EMS3-03e012) with altered expression of maize leaf angle was obtained from the maize EMS mutant library of Qilu Normal University (http://elabcaas.cn/memd/). The mutant bhlh112 was planted in the experimental field of Gansu Agricultural University, we used mutant bhlh112 to continuously backcross B73 for 2 generations and then self-bring for one generation to clear the mutant background. We then used the Sanger Method to preserve the mutated sites during background removal and observe the mutant phenotypes. The experiment was arranged in a complete randomized block design with replicated three time, and technique of the alternately planting of wide (70 cm) and narrow (40 cm) row spacing was used with plant spacing of 25 cm (75000 seeds ha-1). B73 germplasm material was provided by the Maize Breeding Research Group of the Agricultural College of Gansu Agricultural University. The experiment was carried out at the State Key Laboratory of Aridland Crop Science, Gansu Agricultural University.Mutant ZmbHLH112, DNAMAN software to analyze the ZmbHLH112 sequence and sequence alignment, ClustalW programs to perform multiple alignment of amino acid sequences, and MEGA7.0 software to construct multiple alignment results generated by the ClustalW Phylogenetic tree; Using the Neighbor-joining method with select Pairwise deletion, bootstrap set to 1000.We used the NCBI database to find the gene sequence and amino acid sequence encoded by After pollination, three plants of wild-types and mutant with look similar were selected in each plot, respectively, the field phenotypic data  were measured; Meanwhile, ten wild-type and mutants with similar appearance were selected and their ear leaf angle, a leaf angle above the ear, a leaf angle below the ear were measured respectively. Mature ears were harvested at the end of September of the same year and dried to safe moisture before threshing. At that time, the single ear weight, ear length, ear diameter, grain type, grain color, and 100 grain weight were measured, the values shown are averages from three biological replicates. Leaf area size (LAS= LL x LW x 0.75) and leaf direction value (LOV = ∑ (90 - θ i) x (Lf/LL)/N were calculated; θ i: leaf angle value; Lf: leaf spacing; LL: leaf length; N: number of leaves above the ear) .bhlh112 to continuously backcross B73 for 2 generations and then self-bring for one generation as the experimental material, DNA was extracted from V3 stage leaves of the mutant bhlh112 and wild-type B73. Primer premier 5.0 was used to design primers: forward primer: GGAGTTCTGGCACCCTACAT; reverse primer: TTTGGGCGGCATCATGATTT. Using wild-type and mutant DNA as the template, PCR amplification was carried out with Easy-Taq enzyme. The reaction system (20 μL) included Easy Taq 10 μL, ddH2O 7 μL, forward and reverse primers 1 μL each, and DNA template 1 μL. Amplification conditions were pre-denaturation at 94°C for 4 min, 94°C for 1 min, 56°C for 30 s, 72°C for 1 min, after 35 cycles, extension at 72°C for 10 min. PCR amplification products were sent to Shanghai Sangon Biotech Company for sequencing, and the mutation sites were determined by DNAMAN analysis.We used mutant The root, stem, old leaves, new leaves, and the leaf occipital part of the second leaf of maize B73 at the V3 stage were collected and frozen in liquid nitrogen. Three biological replicates for each sampling part in this study.The SteadyPure plant RNA extraction kit was used to extract total RNA and the RNA concentration, purity, and integrity were examined using advanced molecular biology equipment. cDNA (including gDNA) was synthesized by M-MLV (Moloney murine leukemia virus) reverse transcription kit, provided by Accurate Biology Company. Reverse transcription conditions: 37°C for 15 min, 85°C for 5 sec, and 4°C.® Green Pro Taq HS Premixed qPCR kit was used for qRT-PCR, and the internal reference gene Actin . The reaction system was 20 µL: 2xSYBR ® Green Pro Taq HS Premix 10 µL; cDNA 1 µL, forward primer 0.4 µL and reverse primer 0.4 µL, RNase free water 8.2 µL. This was amplified by Illumina qRT-PCR; 2-ΔΔCT calculated the relative expression of genes (We used the SYBR of genes . Reactiobhlh112 with 0 μmol·L-1 (control), 1 μmol·L-1, 10 μmol·L-1 and 50 μmol·L-1; (2) IAA application at five concentrations: B73 and bhlh112 are set to 0 μmol·L-1 (control), 1 μmol·L-1, 10 μmol·L-1, 100 μmol·L-1 and 500 μmol·L-1. Treatment with exogenous plant hormones was carried out from the normal watering to the V1 stage of the seedlings. The treatment was conducted once every other day, with 50 mL poured each time, and the leaves of the corn seedlings sprayed with the corresponding hormones. The leaf angle of each treatment was measured at the V3 stage to screen the optimal concentration of exogenous hormones. 2 cm of the leaf occipital part of the V3 stage at the optimal concentration was collected and frozen in liquid nitrogen, and sent it to Lianchuan biological company for sequencing.The maize seeds were planted with vermiculite in a flower pot with a diameter of 10 cm and placed in an artificial climate room  to determine the phenotypic and physiological characteristics of the seedlings. Two hormone treatment groups were set up in this experiment; (1) ABA application at four concentrations: B73 and For RNA-seq analysis, the second leaf occipital part of the wild-type and mutant plants at the V3 stage were harvested and frozen in liquid nitrogen. Three biological replicates for each genotype and three pooled samples for each replicate were tested in this study. The total RNA of the sample was extracted with TRIzol , and the RNA was isolated and purified according to the manufacturer’s instructions. The quantity and purity of total RNA was controlled with NanoDrop ND-1000 , and the integrity of RNA was detected with Bioanalyzer 2100 ; Illumina Novaseq™ 6000  was used for double ended sequencing according to the standard operating procedures. Clean Data bases produced by each sample more than 43 million; About 94.37% - 97.95% of the clean data can be compared to the number of reads on the reference genome (RefGen_v4) of B73, about 90.48% - 94.27% can only be compared to the number of reads at one position of the genome, and only about 3.51% - 5.14% of the clean data can be compared to the number of reads at multiple positions of the genome. The size of the filtered data accounted for 97.69% of the original data size. The percentage of bases with a quality value of Q≥30 is 97.74% and above. The data presented in the study are deposited in the SRA at NCBI repository, accession number PRJNA856349.In order to verify the reliability of the DEGs, the differentially expressed genes in different comparison groups were selected for qRT-PCR. The method was the same as in 2.5.2; the primer sequence in the Co-expression networks were constructed using the WGCNA package in R. We selected genes with FPKM ≥ 10, gene expression values were imported into WGCNA to construct co-expression modules with default settings, TOM Type was unsigned, used the picksoftthreshold function to select the appropriate soft threshold β = 18, selected the threshold value close to 0.9 of the fitting curve, and constructed the scale-free network distribution. We associated the merged modules with phenotypic traits, selected the genes of the core module top30 according to the weighted values, constructed the protein interaction network between genes with the string website, and drew the network diagram with Cytoscape function.ZmbHLH112 (Zm00001d043248) gene (LOC100277655) is located on maize chromosome 3, with a total length of 1525bp, including 1158bp CDs sequence, encoding 385 amino acids. The protein sequence of ZmbHLH112 was analyzed by Blast in the NCBI database; we found that it had the highest homology with sorghum bicolor (XM_021455582.1 and XM_021455581.1). The above plant protein sequences were compared by MEGA7.0 software, and then a phylogenetic tree was constructed , young leaves (fourth leaf at the V3 stage) and the leaf occipital part of the second leaf were detected by quantitative real-time polymerase chain reaction (qRT-PCR) analysis. ZmbHLH112 had the highest relative expression of the leaf occipital part of the second leaf; relative expression was the lowest in roots and stems  , the leaf angle of B73 was larger than that of bhlh112  was used as the change threshold, and q < 0.05  was used as the standard for screening DEGs. In the control group, a total of 944 DEGs were identified. Among them, 386 genes were significantly up-regulated, and 558 genes were significantly down-regulated, accounting for 40.89% and 59.11%, respectively, of the DEGs , and 76, 21, and 207 significantly down-regulated DEGs for these three categories. For BP, DEGs were enriched in the “obsolete oxidation-reduction process”, “tryptophan biosynthetic process”, and “negative regulation of endopeptidase activity”. The enriched CC terms were primarily “cytosolic small ribosomal subunit”, “anthranilate synthase complex”, and “plasmodesma”. The enriched MF terms were “oxidoreductase activity”, “hydrolase activity”, “protein kinase activity”, “phosphate ion transmembrane transporter activity”, and “catalytic activity”. Further KEGG enrichment analysis revealed that up-regulated genes were enriched in “Stilbenoid, diarylheptanoid and gingerol biosynthesis”, “Linoleic acid metabolism”, and “Phenylpropanoid biosynthesis”; down-regulated genes were enriched in “Benzoxazinoid biosynthesis” and “Plant hormone signal transduction”. These results suggest that exogenous ABA may be involved in leaf angle development of the mutant bhlh112 through plant hormone signal transduction, lipid metabolism, and biosynthesis of secondary metabolites.We performed pathway enrichment analysis on all DEGs in wild-type B73 and the mutant bhlh112 6B. Undebhlh112 had the significantly enriched BP terms “amino acid transmembrane transport”, “obsolete oxidation-reduction process”, “cellulose biosynthetic process”, “exocyst assembly”, “cell surface receptor signaling pathway” and “hexose transmembrane transport”; enriched CC terms were “plasma membrane” and “plasmodesma”; MF terms were “oxidoreductase activity”, “monooxygenase activity”, “amino acid transmembrane transporter activity”, “carbohydrate:proton symporter activity” and “protein histidine kinase binding”.Under IAA treatment 6C, compThe expression levels of some DEGs were evaluated by qRT-PCR in order to validate the reliability of the RNA-seq data Figure 7In order to identify the correlation between the genes obtained by RNA-seq and the phenotypic data of maize leaf angle (LA), the DEGs with FPKM ≥ 10 were selected, and a total of 8994 DEGs were screened to construct the co-expression network. Used the threshold value close to 0.9 of the fitting curve  Table S8To further study the functions of leaf angle related genes, the 12 hub genes of this module were analyzed by GO enrichment and KEGG pathways. In GO analysis, the genes were mainly enriched in “Peptide metabolic process”, “rRNA binding”, “Structural constituent of ribosome”, “Structural molecule activity”, “Mitochondrial large ribosomal subunit”, “Mitochondrial ribosome”, “Plastoglobule”, “Large ribosomal subunit”, and “Mitochondrial matrix”. In KEGG pathway analysis, the hub genes were mainly involved in “Ribosome”.lc2 ) mutant has a larger leaf angle due to increased division of paraxial epidermal cells in leaf joints. However, LC2 is mainly expressed in the lamina joint during leaf development, and is particularly induced by the phytohormones abscisic acid, gibberellin acid, auxin, and brassinosteroids. The mutant lc2 had changed expression level of genes related to cell division and plant hormones due to the lack of the LC2 gene, resulting in the change of leaf angle  is critical in regulating the adaxial/abaxial cell growth of leaves in bidopsis ; it alsoed genes . In the ransport . Our resUGT74D1 affects leaf positioning through modulating auxin homeostasis and regulating transcription of related genes (Rice LEAF INCLUSION1 (LC1), an IAA amide synthase, maintains auxin homeostasis by binding excess IAA with various amino acids, and then regulates the leaf angle  participates in the regulation of the physiological processes required to respond to various abiotic stresses in plants . ABA canIAA24 (Zm00001d018973), a member of the AUX/IAA family, and genes related to cell wall structure, including Zm00001d017033, is a glycine-rich cell wall structural protein. Therefore, we speculate that ABA may regulate maize leaf angle through IAA signal transduction or BR biosynthesis , promote the binding of auxin response factor (ARF) and DNA, and then regulate the elongation of the leaf angle cells. Aux/IAA and ARF the central components of the auxin signaling pathway and play an important role in auxin mediated growth and development  encoded by the TCH4 gene is the primary component that acts on the cell wall and plays an essential role in plant morphogenesis . The free 60s ribosomes and polysomes in the functional defective mutant rice minute-like1 (rml1) were significantly reduced, resulting in plant phenotypic diminution, narrow leaves, and growth retardation. Those results showed that the ribosomal protein RPL3B is required for maintaining normal leaf morphology and plant architecture in rice through its regulation of ribosome biogenesis (Arabidopsis rRNA methyltransferase CMAL (Chloroplast MraW-Like) is involved in plant development, likely by modulating auxin derived signaling pathways. Knockout of the CMAL gene leads to chloroplast functional defects, as well as abnormalities in leaf and root development and in the overall plant morphology , the Fuxi Talent Project of Gansu Agricultural University, China (No. GAUFX-02Y09), and the Lanzhou Science and Technology Bureau (No. 2020-RC-122).We thank the reviewers for the critical review of the manuscripts, and Peng Yunling for her guidance and revision.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.