Prior to coronavirus disease (COVID-19), many Australians experienced extreme bushfires, droughts, and floods. A history of experiencing these events might be a risk factor for increased psychological distress during COVID-19. This study aimed to provide insight into the mental health of Australian workers during the initial COVID-19 outbreak, with an additional focus on whether previous disaster exposure and impact from that disaster is a risk factor for increased psychological distress.A snowball recruitment strategy was used. Participants (n = 596) completed an online survey, which included the Depression Anxiety Stress Scales-21, and questions related to mental health and disaster exposure.Overall, 19.2%, 13.4%, and 16.8% of participants were experiencing moderate to extremely severe depression, anxiety, and stress symptoms, respectively. Multiple regression found that higher depression, anxiety, and stress symptoms were associated with a pre-existing mental health diagnosis; only higher stress symptoms were associated with having experienced a disaster, with impact, in addition to COVID-19.People who have experienced impact from an additional disaster might need additional support to protect their mental health during COVID-19. A focus on the cumulative mental health impacts of multiple disasters and the implications for organizational communities where recovery work is undertaken, such as schools and workplaces, is needed. For example, prior to the COVID-19 pandemic, devastating bushfires ravaged parts of the country, and entire communities were unable to recover before the nation’s focus shifted to the threat of COVID-19. The psychological health impacts of these bushfires are still emerging.3 Rural Australia was predominantly impacted by these events,4 and, in fact, it is disproportionally impacted by extreme climate-related events in general, with bushfires, droughts, and floods common.5 This situation is unlikely to improve as climate-related disasters become more frequent and severe.5 Unfortunately, the Australian COVID-19 literature has largely focused on COVID-19 in isolation from other disasters.Within Australia, the coronavirus disease (COVID-19) pandemic has caused unprecedented disruption to the way most people live, with widespread physical distancing, travel restrictions and border closures, work-from-home mandates, and lockdowns enforced. Previous respiratory endemics, such as severe acute respiratory syndrome coronavirus (SARS-CoV), influenza A/H1N1, and the Middle East Respiratory Syndrome (MERS) posed significant psychological consequences onto many population groups, including symptoms of anxiety, depression, and posttraumatic stress disorder.6 have consistently drawn attention to the high levels of depression, anxiety, and stress-related symptoms being experienced by the general population during COVID-19. Studies from China, Mexico, and Italy have shown that between 15.7–32.4%, 18.7–28.8%, and 8.1–27.2% of people have experienced moderate to severe depression, anxiety, and stress symptoms, respectively.10 Within Australia, 2 studies have used the DASS-21 to assess psychological distress within the general Australian population during the COVID-19 pandemic.11 One study, conducted from March 27 to April 7, 2020, found that 46.3%, 40.8%, and 38.7% of respondents were experiencing moderate to extremely severe symptoms of depression, anxiety, and stress, respectively. However, it should be noted that a significant proportion of the sample had a lived experience of a mental health diagnosis (70%), which likely contributed to the high rates of psychological distress.2 Rates of psychological distress more consistent with international research were found in a separate Australian study, conducted from April 1 to 4, 2020. In this study, 21–35% of the population demonstrated moderate to extremely severe depression, anxiety, and stress, with the reported rates being greater than the population norms.11 These studies highlight the significant psychological impacts of the COVID-19 pandemic on the general population; however, it is likely the risk of experiencing psychological distress varies, depending on a person’s characteristics and vulnerabilities.12International studies focused on measuring psychological distress associated with the COVID-19 pandemic using the Depression Anxiety Stress Scales-21 (DASS-21)11 Similar categories of risk factors have also been shown in international samples.10 Of particular relevance to the Australian context is 1 study that found that a history of stressful situations  prior to COVID-19 was associated with higher levels of depression and anxiety.10 This finding emphasizes the need to consider the mental health impacts of COVID-19 in combination with other life stressors, such as other disasters.The 2 aforementioned Australian studies also give some insights into the factors that place a person at higher risk of experiencing psychological distress. While there was variability between the studies in the specific types of factors that were found to be important, in general, the factors could be condensed into 3 overarching categories: demographic variables , personal and financial vulnerabilities , and COVID-19-specific worries and experiences . Furthermore, a number of variables stand out as being of particular relevance to a risk of increased psychological distress: having a chronic illness or a prior history of mental health diagnosis, and experiencing financial stress.15 At times, the adverse psychological effects can be experienced for prolonged periods after the disaster occurs, particularly when there is severe trauma exposure or additional adverse life events in the aftermath of the event.16 While there is a large amount of literature on disasters and mental health,13 relatively few studies have examined psychological outcomes after multiple disasters. Within Australia, exposure to multiple  natural disasters  has been associated with an increased lifetime risk of panic disorder, while cumulative effects of repeated man-made disaster exposures  have been noted in terms of an increased lifetime risk of obsessive compulsive disorder.17 Research has also identified cumulative effects of repeated disaster exposure  in relation to the risk of suicide at the Australian population level.18 These studies are limited in that the focus on diagnosable psychiatric disorders and suicidality might not be indicative of subclinical levels of distress.15 Taken together, these 2 studies justify a deeper analysis of the cumulative psychological impact of prior disaster experience and COVID-19. Disaster experience should not only include exposure, but also impact; given the level of psychological distress a person experiences following a disaster is impacted by how people are affected by the disaster.20Following a disaster, such as bushfires or floods, the majority of people will recover psychologically and most will not go on to develop a mental health disorder. However, many people will experience strong emotional and physical reactions.22 especially if the capacity of organizations to provide appropriate support can be developed.24 Further, novel approaches to community intervention need to be explored,12 particularly in rural and regional Australia where appropriate treatment services are often limited or unavailable.25 While these are larger issues that will be considered in other manuscripts, this paper specifically focuses on the mental health of Australian workers during the COVID-19 outbreak. It was hypothesized that Australian workers would report levels of anxiety, depression, and stress above the population norms.21 It was also hypothesized that previous recent disaster exposure and impact from that disaster will be a risk factor for higher levels of depression, anxiety, and stress.This paper forms part of a larger program of research looking at the impact of COVID-19 on organizations and workforce and how organizations can be used to deliver appropriate interventions to improve employee preparedness, response, and recovery. Investigating this issue is important as workplaces are an essential part of most people’s social support systems and might offer a level of long-term protection from the psychological impacts of pandemics and disasters,27 As of July 8, 8886 confirmed cases (56% acquired from overseas) and 106 deaths (predominantly in people ages 60 and greater) had occurred in Australia.28Prior to the survey’s distribution, people living in Australia had experienced a series of public health responses that varied depending on geographical location. Restrictions had included border closures, directives to stay at home unless for necessary shopping, health care, exercise and work and study that can’t be done remotely, and the closing of non-essential services and many schools. At the time of the survey’s distribution, these restrictions were gradually being eased. However, by June 20, restrictions were being reinstated in the state of Victoria due to an apparent “second wave” of infection. By July 7, 2020, a second period of lockdown had been introduced for metropolitan Melbourne and neighboring Mitchell Shire.A link to the online survey was distributed via e-mail and social media posts, initially using the authors’ personal and professional networks with subsequent snowball recruitment. The survey was available in English only. Inclusion criteria comprised residing in Australia, being age 18 years and over, and having volunteered or been employed at any time during the period from December 2019 to July 2020. Data were collected from May 29 to July 8, 2020. A follow-up survey will be conducted when restrictions have been lifted. Study data were collected and managed using REDCap electronic data capture tools hosted at The University of Melbourne.The study was conducted in compliance with the Declaration of Helsinki, the National Statement on Ethical Conduct in Human Research  and the University of Melbourne’s COVID-19 Central Human Ethics Committee . All participants provided electronic informed consent before participating. Participants could choose to enter a prize draw for 1 US $100 gift card and/or register their interest in a follow-up survey, by providing an email address. If participants declined to enter the prize draw or register their interest in a follow-up survey, their responses remained anonymous.In total, 616 records were created in REDCap. Responses received after July 8, 2020, were removed from the data set. Of the remaining records, 596 participants were included in the analysis.by myself, with my partner, with pre-school-aged children, with school-aged children, with friends or flatmates, with other family, other), diagnosis of COVID-19 , COVID-19 diagnosis in another person in the house hold , industry of work,29 nature of work , employment type , work location , and changes to employment due to the pandemic .Information was collected on each participant’s age, gender, postcode of residence, living arrangements , and/or a long-term mental health condition .Participants were asked whether they were living with a long-term physical health condition , with 7 items per subscale. Participants were asked to read each statement  and indicate how much the statement applied to them over the past week, on a scale from 0  to 3 (applied to me very much). Relevant items were combined to give a total score for each of the 3 scales, and severity ratings were given  based on this total. The DASS-21 possesses good internal consistency across the subscales and overall scale (α > 0.81), and convergent and discriminant validity has been established.30The survey also included the validated DASS-21,yes, no) and whether they had experienced emotional stress, physical stress, financial stress, damage or loss of property, or other impact because of the disaster(s) they had experienced .Participants were asked whether they had experienced another disaster such as drought, flood, or bushfire in the past 2 years  and living with others . Responses to the pre-existing physical health and mental health condition were limited to yes or no. A new disaster exposure and impact variable, containing 3 categories, was created from the disaster questions: no additional disaster exposure (including participants who answered no to having experienced another disaster in the past 2 years), additional disaster exposure with no impact , and additional disaster exposure with impact (including participants who answered yes to having experienced another disaster in the past 2 years and did report an impact from the disaster).Each participant’s postcode of residence was recoded into the Modified Monash Model categories of 1-7 and subsequently divided into metropolitan (MMM1) and rural, regional or remote (MMM2-7).N and percent (%), while continuous variables are presented as mean (M) and standard deviation (SD). DASS-21 subscale scores were compared to the existing Australian population norms6 using an online t-test calculator. Between group comparisons for continuous variables were completed using analysis of variance (ANOVA). Post hoc tests were undertaken for statistically significant results using Tukey’s Honestly Significant Difference test. Separate multiple regression analyses were conducted to explore factors predictive of depression, anxiety, and stress. The independent variables of age, sex, pre-existing chronic health condition, pre-existing mental health condition, and additional disaster exposure with impact were included in the analysis. For the regressions, the variable “sex” was limited to male and female, whereas the disaster exposure with impact variable was recoded into yes or no from the free text responses.Categorical variables are presented as M = 43.8, SD = 12.9). The majority were from non-metropolitan areas , lived with other people (87.4%), had not been diagnosed with COVID-19 (98.9%), had not lived with someone diagnosed with COVID-19 (99.1%), worked in the health care and social assistance industry (45.0%), were in paid work (98.0%), worked full time (56.5%), and were either working from home (38.9%) or their normal workplace (35.5%). Over half the participants reported COVID-19 had had no impact on their work hours (67.0%). Approximately one-fifth identified as having a pre-existing chronic disease (21.3%) or mental health condition (21.2%). Over half (76.1%) had had no additional disaster exposure.Demographic characteristics of the participants are depicted in 6 DASS-21 scores for depression and stress were significantly higher for participants in this study .P = 0.019, η2 = 0.016). DASS-21 stress subscale scores were significantly higher for participants who had experienced an additional disaster exposure with impact than for those who had experienced an additional disaster without impact (6.8 ± 4.3 vs 4.4 ± 4.0).As shown in Three separate multiple regressions were used to explore factors predictive of DASS-21 depression, anxiety, and stress symptom severity. Sex, age, pre-existing chronic health condition, pre-existing mental health condition, and additional disaster exposure with impact were included as independent variables in each of the analyses. The final multiple regression models are presented in P = 0.041) and having a pre-existing mental health condition . Higher anxiety scores were associated with having a pre-existing mental health condition . Higher stress scores were associated with pre-existing mental health condition  and having reported an impact from the disaster . The predictors for depression, anxiety, and stress explained 25.0%  = 7.008, P < 0.001), 22.7%  = 6.162, P < 0.001), and 21.4%  = 5.939, P < .001) of variance, respectively.Among participants who had experienced a disaster in the 2 years prior to the survey, higher depression scores were associated with being male , the mean DASS-21 scores for depression and stress were significantly higher for participants in this study. Despite this, the levels found in this study were lower than those found in previous Australian research.32 For instance, Newby et al.2 found that 46.3%, 40.8%, and 38.7% of participants reported moderate to extremely severe symptoms of depression, anxiety, and stress, respectively, whereas Rossell et al.32 found that 21-35% of their sample demonstrated moderate to extremely severe depression, anxiety, and stress.This study provides some insight into how the COVID-19 pandemic has impacted the mental health of people living and working in Australia, particularly those who were exposed to a disaster prior to COVID-19. Overall, 19.2%, 13.4%, and 16.8% of participants were experiencing moderate to extremely severe depression, anxiety, and stress symptoms, respectively, as measured by the DASS-21. When compared to the existing Australian population norms33 This was not the case for Newby et al.’s study,2 where the proportion was closer to 70.0%. Further, the majority of participants in this study were from non-metropolitan areas (78.1%), had not been diagnosed with COVID-19 (98.9%), were in paid work (98.0%), worked full time (56.5%), and reported COVID-19 had not impacted work hours (67.0%). The survey used in this study was also distributed at a time when COVID-19 cases in Australia were relatively under control and restrictions were being eased.27 Given these characteristics, it is possible this sample was somewhat protected from increased psychological distress during COVID-19; they were likely experiencing fewer COVID-19 cases and less financial stress than other samples. Additionally, these results should be considered in light of the potential mental health benefits of working.22 Although unmeasured, other protective factors might be contributing to these findings, including workplace , family, or government support.28 Overall, this highlights the need to carefully interpret research in this area.Estimates of the rates of psychological distress are likely to vary based on sample characteristics  and contextual factors . The proportion of participants reporting a mental health condition in the current study was 21.2%, which is representative of the Australian prevalence rates.32 this study found having a pre-existing mental health condition predicted higher severity of depression, anxiety, and stress-related symptoms. This adds to the growing body of evidence highlighting the increased emotional impact of the COVID-19 pandemic on people with a lived experience of mental illness. However, it is also possible that people with a pre-existing mental health condition were experiencing higher levels of psychological distress prior to the COVID-19 pandemic, rather than as a result of the pandemic. It is difficult to differentiate this as no baseline assessment for psychological distress prior to the pandemic was conducted. This is a common limitation in all research related to disasters and mental health, and it will likely be important for future research in this area to adopt a prospective study design to examine prevalence and severity estimates prior to disasters occurring. This study also found being male predicted higher levels of depressive symptoms, but not anxiety or stress. The findings from past Australian research have varied, with 1 study finding being female was predictive of lower depression2 and another study finding lower levels of negative emotions were demonstrated by males.32 However, international research has consistently found that being female is associated with higher levels of stress, anxiety, and depression.10 Contextual issues between countries and surveys may explain some of this variation.The current study also provides insights into the variables associated with higher psychological distress during the COVID-19 pandemic. In particular, and in keeping with previous Australian research,10 Furthermore, it extends this research, by highlighting the need to consider a person’s previous disaster experience when researching mental health outcomes during COVID-19, due to the cumulative effects of repeated disaster on mental health and psychological distress. These cumulative effects have been shown in research unrelated to COVID-19.34 The current study also emphasizes a need to consider how persons are impacted by a disaster, and not just whether they have been exposed to a disaster. Again, this has been suggested by extant literature unrelated to COVID-19.20 That having experienced a disaster in addition to COVID-19, and having experienced an impact from that disaster, was predictive of higher stress symptoms, but not depression or anxiety, could be related to how the DASS-21 measures these symptoms. The DASS-21 conceives stress as a persistent state of over-arousal reflecting a continuing difficulty in meeting taxing life demands, depression as being related to low self-esteem and anxiety as being related to an increased anticipation of negative events.6 It is possible this is reflective of how people emotionally respond to multiple disasters. Alternatively, it is also possible it is too early to see increases in depression and anxiety in our participants; additional life stressors following a disaster are predictive of increased symptomatology over time16; however, the long-term emotional burden of COVID-19 is still emerging. Further research with larger sample sizes would be needed to properly elucidate this.This study also found that having experienced a disaster in addition to COVID-19, and having experienced an impact from that disaster, was predictive of higher stress symptoms, but not depression or anxiety. This adds to previous research into the mental health impact of COVID-19, which has shown a history of more general stressful events  is predictive of higher levels of depression and anxiety.35 Thought will need to be given to the services, organizations, or agencies best placed to deliver such responses, particularly in places where traditional mental health services are limited. In most cases, consideration will also need to be given to including local stakeholders to ensure programs and services are culturally and regionally appropriate.35 Research regarding how to provide mental health responses following disasters is still emerging, but it is hoped the larger program of research surrounding this study will address this issue in more detail, with a focus on how organizations can be used to deliver appropriate interventions to improve employee preparedness, response, and recovery.13While it is difficult to ascertain the level of distress participants in this study were experiencing prior to the pandemic, and whether these levels changed as the pandemic began and progressed, there is a need, regardless, to provide an appropriate response to lessen elevated symptoms of psychological distress. The needs of different subgroups of the population will vary widely, and responses will need to be tailored to an individual’s unique combination of risk and protective factors. Responses will likely need to vary in intensity and encompass multiple levels of intervention  so as to prevent subclinical symptomatology from worsening over time or developing into psychiatric disorders.The results of this study are based on a convenience sample of workers recruited online. Thus, the results are unlikely to generalize to the broader Australian population. Our sample was also small in comparison to other studies in this area, with the majority of participants being female (80.3%), living in a non-metropolitan area (78.1%), and having no additional disaster exposure (76.1%), which might have impacted results. Future research should focus on the mental health impact of having experienced multiple disasters and the implications of this on organizations that may have a role to play in the response, such as workplaces. It would also be helpful to assess in more detail people’s perceived experience of single and multiple disasters, and how this relates to their mental health and well-being.11 When considered alongside other COVID-19 and mental health research, our study highlights the need to provide an appropriate response to lessen a person’s severity of psychological distress; different subgroups of the population will have varied combinations of risk and protective factors, and responses will need to take this into consideration. Importantly, COVID-19 is not occurring in a vacuum, and as climate-related disasters become more intense, frequent, and longer in duration, increasing attention should be given to the cumulative mental health impact of multiple disasters.There is a growing body of evidence that highlights the increased psychological impact of the COVID-19 pandemic on people with a lived experience of mental illness, as well as those who are experiencing, or have experienced, additional stressors .


Methods. 100 patients with their clinical information and IRF data  were retrospectively enrolled in this study, and 94 patients were finally selected for further analysis. Univariate and multivariate analysis were applied to evaluate the potential risk factors for the organ failure of AP. The area under the ROC curve (AUCs), sensitivity, and specificity of the relevant model were assessed to evaluate the prediction ability of IRFs. A new scoring system to predict the organ failure of AP was created based on the regression coefficient of a multivariate logistic regression model. Results. The incidence of OF in AP patients was nearly 16% (15/94) in our derivation cohort. Univariate analytic data revealed that IL6, IL8, IL10, MCP1, CD3+ CD4+ T lymphocytes, CD19+ B lymphocytes, PCT, APACHE II score, and RANSON score were potential predictors for AP organ failure, and IL6 (P = 0.038), IL8 (P = 0.043), and CD19+B lymphocytes (P = 0.045) were independent predictors according to further multivariate analysis. In addition, a preoperative scoring system (0-11 points) was constructed to predict the organ failure of AP using these three factors. The AUC of the new score system was 0.86. The optimal cut-off value of the new scoring system was 6 points. Conclusions. Our prediction model  has satisfactory working efficiency to identify AP patients with high risk of organ failure.Several inflammation-related factors (IRFs) have been reported to predict organ failure of acute pancreatitis (AP) in previous clinical studies. However, there are a few shortcomings in these models. The aim of this study was to develop a new prediction model based on IRFs that could accurately identify the risk for organ failure in AP.  Generally, acute pancreatitis (AP) commonly presents as a mild and self-limiting course with slighter clinical symptoms. However, nearly 20% AP patients present with severe disease, which is correlated with higher rates of organ failure and mortality , 2. AccoIn previous studies, many factors have been reported as independent risk factors for the development of OF in AP, such as lipase, albumin, BUN, and pleural effusion. Moreover, the role of inflammation-related factors (IRFs) in secondary OF is getting more and more attention . The sevIn general, various types of IRFs were potential predictive indicators of OF in patients with AP. However, the forecast performance of each indicator was insufficient in clinical practice. Therefore, the purpose of this work was to develop and validate a simple and effective risk model based on IRFs for the early recognition of OF.Consecutive patients with AP treated in the First Affiliated Hospital of Nanjing Medical University  from 2017.06 to 2018.06 were retrospectively enrolled in our study, and patients referred from other hospitals were excluded in our study. Three criteria were applied to diagnose AP, including persistent abdominal pain, serum amylase or lipase elevation of more than 3 times upper limit of normal, and characteristic ultrasound and/or CT findings. Patients were diagnosed as AP when two or more signs mentioned above were observed. Furthermore, patients with recent surgery (less than 1 week), chronic, traumatic, endoscopic, or recurrent acute pancreatitis, immunosuppression, or immune deficiency were excluded. The principles of the Helsinki Declaration were applied in the performance of our study, and this study was approved by Ethics Committee of the First Affiliated Hospital of Nanjing Medical University.According to the modified Marshall scoring system, serum creatinine was applied to define renal failure (more than 1.9 mg/dL), systolic blood pressure was used to define cardiovascular failure (less than 90 mmHg), and ratio of PaO2/FiO2 was used to confirm respiratory failure (less than 300 mmHg). The duration was used to distinguish transient OF (≤48 h) from persistent OF (>48 h) . PatientThe peripheral blood samples of all patients were obtained within 24 h after admission to hospital. Peripheral blood lymphocyte subset assay was applied to detect the percentage of CD3+T lymphocytes, CD3+ CD4+T lymphocytes, CD3+ CD8+ cytotoxic T lymphocytes, CD16+ CD56+ natural killer cells, and CD19+ B lymphocytes. Blood routine examination was used to obtain white blood cell count and percentage. Blood samples were routinely collected on AP patients admitted to our hospital and then stored and subsequently analyzed for the concentration of 10 cytokines  by using the Human Inflammation Array Q1 (QAH-INF-1-1) . Moreover, other IRF (CRP and PCT) data, several biochemical parameters, and basic clinical information were also collected.t-test or nonparametric tests (Chi-square and Mann–Whitney) were used to compare OF and NOF group. Firstly, the analysis of univariate regression was conducted to evaluate the association between the predictive factors and OF. Second, factors with a P < 0.1 in the univariate regression analysis were included in multivariate logistic regression analysis. A P value of 0.05 were deemed to be statistically significant. The logistic regression coefficients as well as the allocation of scoring points for each predictive factor, based on the regression coefficients, are given in D) between the classification of each risk factor and the basic risk. (4) We set the unit distance (B) for 1 point. (5) Each risk factor was scored, point = D/B.All statistical analyses were conducted by Stata/SE version 10.0 for Windows. Descriptive data were presented as mean ± standard deviation for quantitative variables or absolute and relative frequencies for qualitative variables. Student's A total of 100 patients were enrolled in this study, 94 patients were finally enrolled in the statistical analysis, and 6 patients were excluded due to the lack of IRF data . Related information of clinical parameters was shown in P = 0.003), IL8 , IL10 , MCP1 , CD3+ CD4+ T lymphocytes , CD19+ B lymphocytes , PCT , APACHE II score , and RANSON score  were potential predictors for OF in AP.All patients enrolled in final analysis were divided into two groups according to the occurrence of OF (NOF vs. OF). Univariate analysis was selected to evaluate the association between OF occurrence and IRF levels . The anaP < 0.01) were independently associated with the progression of OF, multivariate logistic regression analysis was subsequently conducted. The related results suggested that IL6 , IL8 , and CD19+ B lymphocytes  were independent predictors for OF development, as well as RANSON scores   .ROC curve analysis were applied to evaluate the predictive value of IRF model for OF, as well as APACHE II score and RANSON score. As shown in Therefore, these three inflammation-related variables  were finally selected to develop a prediction model (IRFs model). All details of IRF model are shown in Results of multivariate logistic regression analysis suggested that IL6, IL8, and CD19+ B lymphocytes are independent predictors for organ failure. Therefore, these three factors were selected into the final model for the construction of the prediction score (IRF score). The prediction scores based on this model range from 1 to 11, and AP patients with higher score had increasing risk to suffer with OF. The risk of AP patients with OF progressively increased as the score increased. ROC curve analysis was applied to evaluate the predictive value of new score system for OF, as well as APACHE II score and RANSON score. The AUC 0.86 (95% CI: 0.78-0.94) of ROC analysis demonstrated a satisfactory discrimination of new score system and higher than other two criteria (APACHE II and RANSON score) . A scoreThe mean ROC curve of tenfold crossvalidation of the new prediction score (IRF score) is shown in As reported in previous studies, organ failure was a common but dangerous clinical manifestations in AP, and organ failure was also regarded as the main reason for the death of AP patients. Therefore, the identification of individuals with organ failure in the early stage was the key step for the treatment of these patients. Different kinds of IRF-induced SIRS were reported to be responsible for the occurrence and development of organ failure in AP, and it has been confirmed that some of these IRFs could function as independent indicators for such pathogenetic process. Several researches suggested that abnormal alteration of some immune cells could play the predictive role in the organ failure process, such as CD4+ lymphocyte, CD19+ B lymphocyte, neutrophil-lymphocyte ratio, and CD14hiCD16-monocytes , 14. SomIn our study, we firstly assess the predictive value of several IRFs for the development of organ failure in AP, including the percentages and/or cell counts of immune cells, the levels of 10 cytokines, and 2 common inflammation biomarkers. Similar to previous studies, the results based on this aspect suggested that the increased levels of IL6, IL8, and CD19+ B lymphocyte were, respectively, correlated with the increased incidence rate of organ failure . IL6 andIn clinical practice, two AP scoring systems, APACHE II and RANSON, were widely used to evaluate to the severity of such disease. In fact, these two models were relatively cumbersome due to the excessive parameters enrolled. Therefore, researchers specialized in AP have been trying to build simple and feasible models to replace them. In recent years, several simple models have been developed to evaluate the severity of acute pancreatitis in previous investigations. For example, Hong et al. published a prediction model for severe acute pancreatitis by using SIRS, albumin, BUN, and pleural effusion ; Qiu et However, our study also has some limitations. Firstly, there were data missing in this study, especially the information of some other IRFs related to AP which were not enrolled, such as IL17, IL12, and M2 monocyte, which might be responsible for the selection bias. Secondly, all data applied in our study were collected retrospectively, which might reduce the reliability of results based on these data. Lastly, because of the funding problem, the sample size of our study was insufficient, not only the total number of all AP patients but also the number of AP patients with organ failure, and the validation cohort for IRF model was also absent. In the future research, we will expand the sample size and set up a validation cohort to further confirm the working efficiency of our prediction model, as well as enroll more potential IRFs.In conclusions, IL6, IL8, and CD19+ B lymphocyte were reliable predictors for the organ failure of AP, and the prediction model has satisfactory working efficiency to identify AP patients with high risk of organ failure. This study may improve clinical prevention and treatment strategies of AP progression.


Although oocyte quality is the dominant factor determining embryo quality, few studies have been conducted to evaluate embryo quality based on the metabolites related to the oocyte. With quantification of the follicular fluid (FF) metabolites, in assisted reproductive technology (ART), this study sought to evaluate the embryo or oocyte quality through an informative approach.An evaluation model consisting of 17 features was generated to distinguish the embryo quality on day 3 post-fertilization, and phosphatidylcholines (PCs) were the key contributors to the evaluation. The model was extended to the patients under different ages and hyperstimulations, and the features were further enriched to facilitate the evaluation of the embryo quality. The metabolites were clustered through pathway analysis, leading to a hypothesis that accumulation of arachidonic acid induced by PCs might weaken embryo quality on day 3 post-fertilization.A discriminating model with metabolic features elicited from follicular fluid was established, which enabled the evaluation of the embryo or oocyte quality even under certain clinical conditions, and the increase of PCs in follicular fluid implies the attenuation of embryo quality on day 3 post-fertilization.The online version contains supplementary material available at 10.1186/s12915-021-01118-w. In recent decades, assisted reproductive technology (ART) has been widely used in treating infertility , 2. PreiTo have a better assessment, there are two fundamental questions, which examination is well acceptable in clinical to ensure oocyte quality and which material is clinically operational to get the quantitative information at a molecular level.Some parameters as maturation, fertilization, and cleavage of oocytes have been gauged in the assessment of the oocyte quality in the clinical setting; however, the influence of these parameters on embryo quality is not predictable. In most in vitro fertilization-embryo transfer (IVF-ET) or intracytoplasmic sperm injection (ICSI) cases, the in vitro cultured embryo on day 3 is a final “product” whose quality is the most decisive factor that affects pregnancy, except for uterus status , 10. MorIn a cycle of IVF-ET or ICSI, the cumulus-oocyte complex is first isolated from the individual’s follicular fluid (FF). The FF provides an essential microenvironment for oocyte development ; thus, iIn this study, a total of 418 FFs were collected and multiple targeted metabolomics was employed to quantify the FF metabolites. By dividing the samples into two groups based on embryo quality on day 3 post-fertilization, the quantitative data were acquired for 136 metabolites from 8 metabolite groups, and machine learning was implemented to develop a random forest (RF) model that could discriminate embryo quality on day 3 post-fertilization at the metabolomics level. The RF model with 17 features was successfully developed, and under certain clinical conditions, RF models with fewer features were generated that could discriminate embryo quality on day 3 post-fertilization. For the first time, integrated targeted metabolomics was employed to demonstrate that some metabolites in FFs were significant indicators of embryo or oocyte quality.Of the 418 FFs collected, 357 FFs were quantified for all targeted metabolomics analysis and 60 FFs were disqualified due to lack of entire information of target metabolomics. Limited by the available volumes of FF, the FF samples taken for individual targeted metabolome varied, and measurements were taken of P180, steroid HOs, water-soluble Vits, fat-soluble Vits, and AAs in 390, 417, 417, 417, 399, and 410 FF samples, respectively.The brief workflow of the data quality control (QC) and statistical analysis are shown in Fig. The boxplots of the metabolite concentrations in the FF are illustrated in Fig. As mentioned above, 357 patients qualified for further investigation, and they came from different medical backgrounds with varied physiological conditions, medical histories, statuses of the uterus and fallopian tubes, and controlled ovarian hyperstimulation (COH) protocols. These patients were divided into three groups—good (G), fair (F), and poor quality (P)—based on the “Methods” section, and the sample size was 145, 52, and 160, respectively. Simply by looking at the clinical parameters measured in the hospital, such as the rate of fertilization, and cleavage, no significant differences were found between groups P and G, except in 3 parameters: body mass index (BMI), retrieved oocytes (RON), and estradiol on the human chorionic gonadotropin injection day (Ie2). Importantly, these parameters, either alone or in combination, could not be successfully employed to predict embryo quality on day 3 post-fertilization , 2 BAs, 1 HO, 1 Vit, and the sum of hexose, were regarded as the candidates whose abundances were significantly different between groups G and P. The abundance differences for most metabolites with significant changes between G and P were remained within a narrow range, abundance ratios (G/P) less than 1.2, and there were only two metabolites, Cortisone and PCaaC42:1, that had the ratio close to 1.2. Hence, univariate receiver operating characteristic curve (ROC) analysis was further employed for the individual metabolites to determine whether a metabolite abundance was able to differentiate the embryo qualities. The results of the ROC analysis are illustrated in Additional file Wilcoxon rank-sum tests were individually applied to the 136 quantified metabolites to explore whether the abundance of a metabolite in group G was significantly different from that in group P. In Fig. Under unsupervised conditions, principal component analysis (PCA) was implemented in multivariate analysis. In this PCA, 60% of the 136 targeted metabolites plus 21 clinical parameters were regarded as the principal components; however, the two groups, G and P, were not well separated by these components , resulting in these metabolites being clustered into 3 modules, with the black module containing 81 metabolites, the gray having 5, and the cyan possessing 3  and Beijing Genomics Institute (BGI) (reference BGI-IRG 20035). The oocytes were retrieved transnationally under ultrasound guidance in routine-assisted reproductive therapy. The transparent FF was collected and stored at −80°C until further metabolic analysis. The consequent embryos on day 3 after oocyte retrieval were evaluated upon the Puissant scoring system with slight modifications . A high-v/v = 1:1) was mixed with FFs on a 96-well plate, and the resulting supernatant was taken for further analysis after vortex and centrifugation. For the 5 water-soluble Vits, the FFs were mixed with a solution of methanol/water (v/v = 9:1), followed by vortex and centrifugation to obtain the supernatant. The steroid HOs were extracted by methylene chloride, dried with nitrogen, and resolved in methanol/water (v/v = 1:3). For AAs, 0.1 g/ml sulfosalicylic acid was added to the FFs for protein precipitation, and the supernatant after centrifugation at 4000 rpm was directly loaded into UPLC.All the collected FFs were thoroughly vortexed after thawing and centrifuged at 4°C for 5 min at 5000 g. The supernatants were directly used for metabolite extraction. According to the manufacturer’s user guide, the 188 metabolites were extracted from the FFs by Biocrates® Absolute IDQ P180 kit . An aliquot of FF placed on the membrane of a 96-well microplate was fixed with phenyl isothiocyanate for derivatization, and the derivative products were eluted by 5 mM ammonium acetate in methanol. For the 5 fat-soluble Vits, the solution of methanol/acetonitrile  at negative mode. The information regarding the separation of all the metabolites is listed in Additional file Three types of triple quadrupole mass spectrometers were adopted for targeted metabolomics, Xevo TQ-S  for fat- and water-soluble Vits, QTRAP 5500 mass spectrometer  for AAs, and steroid HOs and QTRAP 4500 mass spectrometer  for the P180 kit. The MS parameters for each machine were optimized during the experiments, and the https://www.metaboanalyst.ca) was applied to evaluate the batch effect caused by the parallel experiments. According to the “80% rule,” a feature was regarded as a missing value if it was not detected in less than 80% of samples [The quality control samples, either provided by a commercial source or prepared from extracts of the pooled FFs, were alternatively placed every 16–19 samples during the experiment using a mass spectrometer. Whether the MS/MS data were accepted for further informatics analysis was based on the data quality control criteria, either Biocrates rules for the P180 kit or CV less than 30% in our laboratory . PCA wit samples . The misTo evaluate which FF metabolites were significantly different between the different groups, univariate analysis of the targeted metabolomics data was processed with Wilcoxon rank-sum test using MetaboAnalyst 4.0. For the same purpose, multivariate analysis was implemented with two approaches, unsupervised and supervised. Under the unsupervised condition, PCA was employed using the prcomp package to explore which metabolites primarily contributed to distinguish the different groups. Under the supervised condition, rfFuncs, an algorithm based on RF and written for R with the Caret package, was used to reduce the dimensions of the features. The optimal iteration runs to select the features were first optimized from 50 to 2000, then the lowest runs at the saturation phase were set as the optimal one, 1000 in this study. With 10-fold cross-validation repeated 3 times, RF models were built with the feature pattern recommended by rfFuncs to discriminate the different groups. Corresponding test sets were evaluated with ROC analysis, and AUC in ROC was a decisive parameter to judge whether a prediction was acceptable. Two-tailed statistical analysis was applied in all the involved tests.https://www.genome.jp/kegg).With the analysis of WGCNA using R, all the targeted metabolites were grouped into several modules based on their abundance accordance. Furthermore, the TOM was employed to estimate the correlation efficiency of the two metabolites in a module and to deeply examine a potential network of the metabolites in such a module. Finally, the metabolites in the network overlapped with those in the prediction model, and the overlapped metabolites were inputted into KEGG database  the patient ages < 35, (B) the patient ages ≥ 35, (C) the patients received long protocol, (D) the patients received ultralong protocol, (E) the patients received GnRH-ant protocol. The representativeness of X-axis and Y-axis is as same as Figure Additional file 6: Further dimensional reduction of the features consisting of metabolites and clinical parameters with rfFuncs in each subgroup. A total of 7 subgroups upon the clinical parameters are selected, (A) the patient ages < 35, (B) the patient ages ≥ 35, (C) the patients received long protocol, (D) the patients received ultralong protocol, (E) the patients received GnRH-ant protocol. The representativeness of X-axis and Y-axis is as same as Figure Additional file 7: The morphology of the embryos on day 3 after fertilization. (A) The morphology of the embryo evaluated as G group on day 3 after fertilization (8C-I); (B) The morphology of the embryo evaluated as P group on day 3 after fertilization (IV).


Despite its reduced sensitivity, sputum smear microscopy (SSM) remains the main diagnostic test for detecting tuberculosis in many parts of the world. A new diagnostic technique, the magnetic nanoparticle-based colorimetric biosensing assay (NCBA) was optimized by evaluating different concentrations of glycan-functionalized magnetic nanoparticles (GMNP) and Tween 80 to improve the acid-fast bacilli (AFB) count. Comparative analysis was performed on 225 sputum smears: 30 with SSM, 107 with NCBA at different GMNP concentrations, and 88 with NCBA-Tween 80 at various concentrations and incubation times. AFB quantification was performed by adding the total number of AFB in all fields per smear and classified according to standard guidelines . Smears by NCBA with low GMNP concentrations (≤1.5 mg/mL) showed higher AFB quantification compared to SSM. Cell enrichment of sputum samples by combining NCBA-GMNP, incubated with Tween 80 (5%) for three minutes, improved capture efficiency and increased AFB detection up to 445% over SSM. NCBA with Tween 80 offers the opportunity to improve TB diagnostics, mainly in paucibacillary cases. As this method provides biosafety with a simple and inexpensive methodology that obtains results in a short time, it might be considered as a point-of-care TB diagnostic method in regions where resources are limited. Mycobacterium tuberculosis (Mtb), is a curable and preventable disease that primarily affects the lungs  and remains one of the leading causes of death from a single infectious agent. At least one third of the estimated annual cases globally are not detected and treated early ,,6,7,5,6,In developing countries, where more than 95% of TB cases and deaths occur ,10. BecaMtb . T,6. T5,6]Due to the difficulty of obtaining single samples with high volumes that would allow the evaluation of the GMNP and Tween 80 concentrations of interest in NCBA, the 46 samples were divided into two equal parts: 23 samples were processed independently. The remaining 23 samples were randomly divided into 7 pools. The 7 pools were grouped as follows: pool 1, with 7 samples; pool 2, with 4 samples; pool 3, with 3 samples; pool 4, with 3 samples; pool 5, with 2 samples; pool 6, with 2 samples; pool 7, with 2 samples. The mean volume of the 23 samples, evaluated independently, was 3.71 mL (SD = 2.78), and the mean volume of 23, grouped into seven pools, was 15.85 mL (SD = 3.52).Using aliquots of all individual and pooled samples, a total of 225 smears were prepared and examined, as shown in Firstly, clinical samples arrived at CAIEP and were processed by SSM. Secondly, the samples were homogenized, and then we included GMNP in the samples at different concentrations (to detect and capture AFB from sputum samples). Finally distinct concentrations of Tween 80 were included as a dispersant of GMNP-AFB complexes to evaluate its effect from high to low GMNP concentrations. Each step is described sequentially for both the method and results sections.For AFB quantification, all smears were examined with a brightfield microscope (Carl Zeiss Primo Star model) under an oil-immersed objective (100x). The bacillary load on the stained slides was counted as the total number of AFB observed per field, in 100 fields on each slide, for SSM, NCBA, and Tween 80.SSM with the Ziehl-Neelsen (ZN) staining technique was performed with an aliquot of approximately 20 µL of each sample and each pool, according to the current guidelines for low-income countries . After sOnce the SSM was completed, the rest of the samples were homogenized with 0.4% NaOH and 4% NALC at a 1:1 proportion, which are the optimized concentrations obtained from our previous study . After hAliquots were mixed by inversion followed by incubation for 10 min at room temperature (~18 °C). Microtubes were then placed in a 3D-printed magnetic rack to separate the GMNP-AFB complex, and the supernatant was discarded. A smear was prepared by transferring 20 µL of the concentrated sputum to a slide that was then stained using the ZN technique .v/v concentration and 12 at 20% [v/v) and two incubation times (3 and 10 min) experimentally determined by the authors to favor the modification of the mycobacterial cell wall, without exceeding the interaction time of the aliquots with the homogenizing solution and Tween 80. We analyzed 32 aliquots of approximately 1 mL obtained from 4 pools: 16 were concentrated with GMNP at 1.5 , and 16 were concentrated with 2.5 mg/mL .To analyze the effect of Tween 80 on the dissolution of the GMNP-AFB complexes formed during NCBA, two procedures were developed: (a) For high concentrations of GMNP and Tween 80, initially, 24 aliquots of 20 µL (two from each of the 12 independent samples) were processed with a high GMNP concentration (10.0 mg/mL), followed by the NCBA method. Subsequently, these 24 aliquots were separated into 2 groups: 12 were incubated for 3 min at room temperature with Tween 80 at 10% 2 at 20% . (b) DueFor AFB quantification, all the aliquots (20 µL) were smeared onto a glass slide and stained with the ZN .AFB quantification of SSM used the standard WHO guidelines, as shown in To analyze trends in the number of AFB quantified in SSM, NCBA (0.25–10 mg/mL GMNP concentrations), and Tween 80 (5–20% concentrations and 3–10 min incubation times), descriptive statistical analysis was conducted in three stages: univariate, bivariate, and multivariate. The analyses were expressed by means, SD, standard errors of the mean (SEM), and by medians and interquartile ranges (IQR). The latter is due to the non-parametric behavior of the data, thus allowing to adjust for the dispersion of the results by discarding extreme values far from the median.t-tests. To compare the effects of Tween 80  and the NCBA assay (with different GMNP concentrations), a multifactorial ANOVA was performed on the number of AFB quantified, followed by Bonferroni’s or Tukey’s HSD post-hoc tests [To determine whether NCBA at different GMNP concentrations had a favorable effect on AFB quantification and field classification in smears, with respect to SSM, McNemar’s test was conducted . Pairwisoc tests . When thoc tests . FinallyAll analyses were performed using SPSS version 21 (IBM Corp)  at a 95% confidence interval , and the graphs were made in Excel (Microsoft Office 365).From each smear of all samples processed by SSM and NCBA-GMNP (regardless of concentration), AFB were counted per field. Based on this count, AFB groups were categorized as: negative (non-AFB), scanty (1–9 AFB), 1+ (10–99 AFB), 2+ , and 3+  ,6. For sIn SSM, the median for the number of AFB per field analyzed was 9 , which was lower compared to NCBA at low GMNP concentrations . On the other hand, at high GMNP concentrations , the median number of AFB in NCBA was lower than in SSM .The greatest variability in AFB detection was shown in the proportion of observed fields that were determined to be negative by SSM but were determined to be positive with NCBA, varying between 13.9–65.3%. The AFB count obtained as scant bacilli levels in SSM, increased between 6.5–38.2% at 1+ and 2+ with NCBA, and the SSM categories at 1+, increased to 2+ and 3+ by 1.8–27.1% .We found significant differences among the GMNP concentrations evaluated in the NCBA assay. Low GMNP concentrations (≤1.5 mg/mL) increased the proportion of the number of AFB per field by more than 25%, as can be seen in Regarding the improvement of the number of AFB per field, we also observed an increase in the average AFB count in the NCBA at low GMNP concentrations compared to SSM. After the initial classification of SSM smears as negative, scanty, 1+ or, 2+ and 3+, in the NCBA we observed 2–7, 5–16, 56–79, and 188–2030 more AFB per field, respectively. With high GMNP concentrations, the average AFB number compared to SSM, using the same categories, was reduced, as shown in In all cases, NCBA increased the percentage of the classification of positive fields obtained in SSM, mainly in those where the AFB loads were insufficient to classify them as positive. Initially, we evaluated high concentrations of Tween 80 (10 and 20%), and we observed that these concentrations did not sufficiently disaggregate GMNP (at 10 mg/mL)-AFB complexes; hence, AFB observation and quantification were not improved.On the other hand, with GMNP at 2.5 mg/mL, the combination of Tween 80 concentration from 10–20% and 10 min of incubation time, the number of AFB quantified was lower. In addition, the mean values of AFB counted were not significantly better compared to NCBA (black line in The use of Tween 80 as a dispersant of GMNP-AFB complexes in NCBA increased the number of AFB by 3–199% compared to NCBA (without using Tween 80) and 29–445% compared with SSM . The comIn contrast, using the combination of GMNP (2.5 mg/mL) and Tween 80 (5–20%), the percentage increase of bacilli was lower in NCBA by 7–76% and in SSM by 29–113%.Furthermore, independent of the concentration of Tween 80, with an incubation time of 10 min, the percentage of AFB quantification decreased compared to a low incubation period .Despite its reduced sensitivity, in many developing countries, SSM remains the main diagnostic test for detecting TB, as well as for assessing anti-tuberculosis treatment progress .Our previous study demonstrated that, during the NCBA-GMNP assay, complexes are formed with AFB, which in turn induces cell enrichment of positive sputum samples  and imprPseudomonas aeruginosa [Salmonella enterica [Staphylococcus aureus [Surfactants, such as Tween 80, are a type of organic compounds with polar (hydrophilic) and non-polar (hydrophobic) groups that disintegrate nanoparticles through the repulsive forces of static electricity, steric hindrance, and Van der Waals force by their surface adsorption ,51. By cruginosa , Salmoneenterica , and Stas aureus , among oIn this study, we used positive sputum samples to improve the NCBA assay by evaluating GMNP and Tween 80 at different concentrations to improve AFB detection and quantification compared with conventional SSM. According to current guidelines ,6, we weMtb cell wall receptors can form complexes with the GMNP by Brownian motion, hydrodynamic force, and physicochemical and molecular interactions [We hypothesize that low GMNP concentrations do not saturate the matrix sample, allowing AFB their easy mobility to be captured and concentrated. In a study by Phenrat el al. , they evractions . These fRegardless of the GMNP concentration used during the NCBA assay, we have observed that GMNP-AFB complexes can cluster in such a way that not all AFB are visible during microscopic analysis. While some fields are highly positive, others may have scanty bacilli, as shown by the proportion of positive fields . HoweverThe addition of Tween 80 during NCBA-GMNP assay provided better observation and quantification of AFB in microscopy analysis . The comMycobacterium avium complex (MAC), cells become morphologically smoother and elongated, due to the loss of its various constituents, including arabinose, glucose, mannose, arabinomannan, and phthiocerol dimycocerosate (PDIM) [The increased visualization and quantification of AFB is facilitated by two factors. (i) The low GMNP concentration does not saturate the sample matrix, as explained above and (ii) the ability of Tween 80 to increase the separation between GMNP  and chane (PDIM) ,57,58.The remodeling of the mycobacterial cell surface caused by Tween 80 presents a double advantage in NCBA. In addition to the mentioned disaggregation of GMNP-AFB complexes it also functions as an additive that could improve biosafety during laboratory work. This is because Tween 80 influences the loss of virulence factors, such as PDIM  and TDM Mycobacterium smegmatis (Msm), determining that this efficiency decreases linearly with increasing bacterial concentration [The usefulness of Tween 80 at low GMNP concentrations may also be favored in TB paucibacillary samples. In this study, we determined that when the number of AFB is greater than 10,000 per field, the quantification and proportion of AFB is reduced . This fintration .Mtb cell wall glycolipids or glycoproteins to interact with GMNP [M. smegmatis (Msm) and GMNP have been observed during transmission electron microscopy (TEM) analysis, where GMNP binds on the bacterial surface [Mycobacterium avium complex is gradually reduced by the loss of GPLs [On the other hand, the evaluation of high GMNP concentrations (≥1.75 mg/mL) continued causing a greater number of GMNP-AFB complex clusters, which reduced the visualization and quantification of AFB , as wellith GMNP  and on tith GMNP . Since tith GMNP ,22. Supp surface . The stu of GPLs .However, in our previous study, where high GMNP concentrations 10 and 20 mg/mL) were analyzed, the gain in AFB counts was 47% in NCBA, with respect to SSM with a concentration of 10 mg/mL  mg/mL we.Mtb infection to the various degrees of disease severity. When in an individual TB infection progresses to a subclinical form, in which there are no typical symptoms of TB, the SSM is often negative, because they are usually paucibacillary, but cultures can be positive [Mtb-positive with GeneXpert MTB/RIF and 58 (34.52%) with culture [The clinical and microbiological classification of TB infection is currently considered as a dynamic continuum from positive . Numeroupositive  have doc culture .For paucibacillary cases, such as pediatric TB, or people with comorbidities , the SSM result is usually negative due to the low number of bacilli in the samples . In thesAccording to our results, using 1.25 mg/mL GMNP concentrations incubated with Tween 80 at 5% for 3 min allows optimal capture efficiency to detect a higher number of AFB in fields where they were scanty in SSM. Thus, the NCBA-GMNP assay represents an advantage with respect to SSM (due to its low sensitivity) in the detection of subclinical or paucibacillary cases, with the same performance as of standard culture  and moleIn addition, NCBA shows numerous advantages compared to SSM: (a) the ability of NCBA to form GMNP-AFB complexes and concentrate AFB , throughThe limitations of the study are the following: (1) we could not analyze more positive samples due to the deviation of human, material, and infrastructure resources to combat the COVID-19 pandemic. Health staff did not carry out active and passive TB case-finding, and the access to the biosafety laboratory was limited . (2) TheCell enrichment of paucibacillary sputum samples by combining NCBA-GMNP (1.25 mg/mL) incubated with Tween 80 (5%) for 3 min improves capture efficiency and increases AFB detection up to 445% over conventional SSM. NCBA with Tween 80 offers the opportunity to improve diagnostic accuracy and biosafety with a simple and inexpensive methodology that obtains results in a short time, so it might be considered as a point-of-care TB diagnostic method in regions where resources are limited. NCBA can become an essential tool in detecting paucibacillary cases, such as pediatric cases and TB patients with comorbidities , which usually give negative results in SSM. Further studies will focus on analytical and diagnostic validation to determine the contribution of NCBA in TB case detection.


The results show thatP2l12 in the Jimusaer sag is a mixedfine-grained sedimentary system composed of sandstone, mudstone, andcarbonate, which can be divided into seven types: dolomitic mudstone,calcareous mudstone, mudstone, mixed fine-grained rock, argillaceouslimestone, sandstone, and argillaceous dolomite. The organic mattertype of P2l12 in well Ji-X is dominatedby types I and II, and this is the best source rock in the whole explorationarea of the Jimusaer sag. The overall oil saturation is relativelyhigh, but the maturity of crude oil is low, and the overall oil qualityis heavy, which is mainly controlled by the sedimentary environmentand maturity of source rocks. Lithology and reservoir physical propertiesare the key to control oil content. The high-quality light oil reservoirlithology is argillaceous dolomite and sandstone. The higher the contentof macropores and mesopores, the weaker the heterogeneity of the porestructure, and the better the oil content in the reservoir. Thereare four light oil sweet spots in the upper part , and there are excellent source rocks with high HI andhigh organic matter content near each sweet spot. This discovery ofshale oil enrichment regularity will effectively guide the developmentof shale oil in continental lacustrine basins in other parts of theworld.The Lucaogou Formation(Fm) in the Jimusaer depression is the firstlarge-scale development of a terrigenous clastic sedimentary shaleoil reservoir in China. Nearly one billion tons of shale oil resourceshave been discovered. However, the current exploration and developmentis concentrated in the eastern part of the sag. The limited geologicalunderstanding in the western area has restricted the prediction anddevelopment of “sweet spots” for shale oil. To helprectify this, we have studied the petrology, geochemistry, oil content,and pore properties of the second part of the first member (Mbr) ofthe Lucaogou Fm (P At present, there have been manyachievements in shale oil-related research, but there are still manyunsolved key problems related to shale oil exploitation. For example,the conventional classification schemes for shale pore types mostlyadopt the decimal classification method suitable for coal proposedby Hodot in 1996 or the pore classification scheme for chemical syntheticmaterials proposed by IUPAC in 1972.2 However,the shale reservoir is obviously different from coal in the pore structureand mineral composition, and even more different from the chemicalsynthetic materials.3 Therefore, a poreclassification scheme suitable for the shale oil reservoir is urgentlyneeded.The discovery of Bakken shale oil in the United States has madethe exploration and development of shale oil a hot topic.5 Previous studies have focusedon lithology and lithofacies,6 geochemicalcharacteristics of source rocks,8 reservoir physical properties,10 shale oil properties,11 and oil-gas migrationcharacteristics of Lucaogou Fm,12 but theresearch has been mostly concentrated in the eastern slope area, withlittle work in the western sag area.A series of major breakthroughs have been made in shaleoil explorationand development of the Lucaogou Formation (Fm) in the Jimusaer depressionof the Junggar basin, and several wells have achieved commercial oilflow. The latest exploration shows that its reserve scale is one billiontons, and the exploitation potential is thus huge. However, the complexsedimentary environment and low thermal maturity have limited itseconomic development.13 Hence,rock types and properties are different. (2) The burial depth of theeastern slope area is shallower than in the western sag. The differencein the burial depth may lead to different temperature and pressureconditions and affected parameters, such as source rock maturity andreservoir physical properties. Therefore, the lithology, reservoirphysical properties, source rocks, and oil content in the westernsag are of great significance for expanding the exploration and developmentpotential of lacustrine shale oil in the Jimusaer sag.During the deposition ofLucaogou Fm, the differences between thewestern sag and the eastern slope are as follows: (1) lake delta faciesare developed in the eastern slope area, while semi-deep lake faciesare developed in the western sag.S1, OSI(S1/TOC × 100%), etc.] used to characterizethe oil-bearing property in shale can only indirectly reflect thefree oil content. The application of two-dimensional NMR (nuclearmagnetic resonance) and QGF (quantitative grain fluorescence) technologyin the evaluation of the free oil content provides a way for unconventionalshale oil development.14 The Lucaogou Fmin the study area is characterized by variable lithology, diversemineral composition, and frequent interbedded distribution of thesource rock and reservoir, 2D NMR can solve the above problems bydistinguishing the solid organic matter and oil signal. Combined withfluorescence thin section examination, fluid mobility can be identified,which provides a strong basis for sweet spot identification.At present,the identification standard of “sweet spot”of continental shale oil in Jimusaer is not perfect, and the problemssuch as large reservoir depth, thin drilling target layer, and highsand adding strength result in a low drilling encounter rate and lowfracturing efficiency, and it is difficult to reduce the cost andincrease the efficiency of exploration and development of shale oil.Therefore, how to effectively identify the “sweet spot”is a key problem. Free oil is the main producing part of the volumefracturing development of horizontal wells, and its content representsthe material basis for exploitation. Conventional parameters [suchas total organic carbon (TOC), 2l12 of the typicalwell (well Ji-X) in the western sag was taken as the research object.Through thin section identification and X-ray diffraction (XRD) ofwhole rock and clay minerals, the mineralogical and petrological characteristicswere analyzed and the main lithologies were determined. The geochemicalcharacteristics of source rocks were analyzed through TOC, pyrolysis,maceral identification, and the differences in petroleum generationpotential between the eastern and western source rocks were compared;the micro- and nanopore characteristics and pore structural characteristicsof the shale oil reservoir were studied by high-pressure mercury injectionand scanning electron microscopy (SEM). The oil contents were analyzedin detail by fluorescence thin section, 2D NMR, fluorescence QGF,and fluid saturation tests. On the basis of the above, the verticaldistribution characteristics of the oil content and its main controllinggeological factors have been systematically evaluated, and the accumulationmechanism of shale oil is summarized. The research results shouldbe of great significance for shale oil exploration in the JunggarBasin and potentially other regions in the world.Based on this, P22, which is a half graben with high in the east and lowin the west  from bottom to top, with the development ofthe upper and lower sweet spots. The lower sweet spot is located inpart 2 of Mbr 1 of the Lucaogou Fm (P2l12). The terrigenous clastic supply is sufficient during thedevelopment of this section18 and consistsmainly of interbedded sandstone, mudstone, and dolomite .3.222 First, dilutehydrochloric acid was used to dissolve carbonates, and then, the TOCcontent was determined using a CS-230 analyzer, with an infrared detectorto quantify the CO2 formed from the combustion of the organiccarbon. A Rock-Eval 6 was used to determine S1, S2, and Tmax (the temperature of maximum pyrolysis S2 yield) according to the Chinese standard (GB/T 18602-2012).23S1 was measuredat 300 °C for 3 min, and S2 was determinedafter the temperature was programmed to 550 °C.All 43 sampleswere tested for bulk geochemistry, including the TOC and Rock-Evalpyrolysis. In order to determine the total oil content, 19 sampleswere selected for TOC and Rock-Eval pyrolysis tests after solventextraction. The TOC content was determined according to the Chinesestandard (GB/T 19145-2003).24 Inthis study, the percentage of a single maceral was determined usingthe same polishing blocks cut perpendicular to the stratified surface.Maceral analysis was performed using a Leica MPV microscope with incidentwhite light and blue light, 50 times objective lens, and oil immersionmethod. At least 1000 points are calculated for each polished block.25The maceralcompositions of 21 samples were determined according to the Chineseindustry standard (SY/T 6414-2014).3.3The pore structureanalysis was performed on 22 samples using a PoreMaster-60 type automaticmercury pressure apparatus. The contact angle between the mercuryand the sample surface was 140°, the surface tension of mercurywas 480 dyn/cm, and the pore diameter range was larger than 3.0 nm.Field-emission SEM of FEI quanta 450 is used to classify the reservoirspace types.10 Pores have self-similaritywithin a certain scale range, and pores of different scales have differentfractal dimensions. Therefore, fractal theory can be used to guidethe division of pore boundaries. The calculation of the pore fractaldimension is as follows:21High-pressure mercury injection experiments caneffectively describe the pore size and pore-throat combination relationship,which are the key factors to control the permeability of the shaleoil reservoir.The relationshipbetween the pore radius and the experimental pressureis as followsVm) with a cylinder with a height of L and a radius of R, the following relationshipexists between Vm and DD is the fractal dimension, r is the pore radius, nm; p is the experimentalpressure, psi; C is the proportionality constant;and K is the slope of Vm and log10(p/106.633).According to the fractal definition, when measuring the porevolume(3.426Oil content tests included2D NMR (43 samples), fluorescence QGF test (43 samples), fluid saturationtest (22 samples), and crude oil group composition analysis (33 samples).T1–T2 map into four parts, which are solid organic(T1 > 10 ms and T2 < 0.2 ms), light crude oil (T2 > 0.2 ms and T1 > 10 ms), hydroxyl-richcompounds (T2 < 0.2 ms and T1 < 10 ms), and water (T1 < 10 and 1 ms > T2 >0.2 ms).27A NMR instrument with an experimental frequency of 23 MHzwas used to quantitatively divide the organic matter abundance andlight hydrocarbon fluid in samples. The advantage of 2D NMR lies inthe ability to divide the 28 First, thesample was crushed to a size of 80–100 mesh, weighed 2 g, andthen added into 20 mL of dichloromethane solution, and the extractionsolution was obtained by ultrasonic vibration. QGF-E represents thefluorescence characteristics of the hydrocarbon extraction solutionadsorbed on the surface of reservoir particles. λmax and QGF-E intensity are the two most important parameters. The formerreflects the composition and density of crude oil, and the lattermainly represents the oil saturation.14 TSF  technology can be used to detectthe three-dimensional fluorescence spectrum of the QGF-E solution,which can reflect the hydrocarbon composition information more comprehensively.TSF and QGF-E technology can be used to judge the hydrocarbon propertiesand conduct fine oil source correlation.29 Oil characteristics can be determined by MAX-EX, R1, TSF-MAX, MAX-EM , and R2 .30 The lower the aromatic content and TSF-MAX value, the higher theAPI and the lighter the oil quality. The smaller R1 and R2, the higher the maturityof crude oil, and the lower the density of crude oil.The fluorescent test was carried outaccording to the Chinese industrystandard (SY/T 7309-2016).The fluidsaturation is determined by the distillation extractionmethod. Toluene is used to extract oil in the oil–water core,dry it, and get the oil saturation according to the weight of thecore before and after extraction. At the same time, the water in thecore is evaporated out by the extraction process, condensed, and gatheredin the scale tube of the water catcher. After the water in the tubedoes not increase, the volume of the water is read out, and then thewater saturation is calculated.31 First, n-hexane was used tofilter the asphaltene solution in the rock soluble organic matteror crude oil. The filtrate was eluted with n-hexane,dichloromethane and n-hexane (volume ratio 2:1),anhydrous ethanol, and chloroform to obtain saturated hydrocarbon,aromatic hydrocarbon, and nonhydrocarbon in turn. Then, the solventof each component is volatilized until a constant weight is achieved,and the content of each fraction is normalized to 100%.Gas chromatography is used toanalyze the bulk composition of theoil according to the Chinese industry standard (SY/T 5119-2016).44.12l12 in well Ji-X were analyzed. The results showthat this succession is a set of mixed fine-grained sediments of sandstone,mudstone, and carbonate under the combined action of mechanical deposition,chemical deposition, and biological deposition in a saline lake environment,with thin lithology thickness and frequent interbedding ,potash feldspar , plagioclase , calcite , dolomite , siderite , pyrite , and clay minerals  1.32 Clay minerals aremainly smectite  and illite , and other minerals are relatively minor, including I/Smixed layer mineral, kaolinite, and chlorite. The higher content ofsmectite is closely related to alkaline volcanic materials.33The mineralcomposition is complex with strong heterogeneity andis dominated by felsic fine-grained rocks 3, which 4.213 According to the Ro value calculatedby the Tmax value, the source rock isgenerally mature, reaching the oil window (The TOCcontent covers a range from 0.42 to 19.12% (averaging 3.89%) , which is higherthan that in the slope area (averaging 3.49%).l window 2.2l12 in well Ji-X is thebest (type I–II)  4a,b. Theoportion 4c. In adJimusaer4d.4.34.3.1SEM shows that the poresin the samples can be divided into three categories: primary pore,secondary pore, and fracture, which can be divided into the residualintergranular pore, dissolved intergranular pore, dissolved intragranularpore, intercrystalline pore, organic pore, and microcrack 5a–f. The4.3.2Vm) and log10(p/106.633), indicating thatthe pore space of the samples has multifractal characteristics, andthe pore diameters corresponding to the turning points are 600, 60,and 20 nm, respectively ; V2, mesopore volume(60–600 nm); V3, transition porevolume (20–60 nm); and V4, microporevolume (<20 nm); D1, D2, D3, and D4 are fractal dimensions of macropore, mesopore, transitionpore, and micropore, respectively.The fractal dimension is oftenused to quantitatively characterizethe complexity of the pore space. The fractal dimension of differentpore sizes is related to reservoir physical properties, but it isdifficult to fully represent the complexity of the whole sample. Therefore,taking the pore volume ratio of different pore sizes as the weightof the corresponding fractal dimension, the comprehensive fractaldimension is obtained by the weighted sum of the fractal dimensionsof different pore sizes.The pore comprehensive fractaldimension of samples with differentlithologies is between 2.49 and 4.58 3. GeneraThe pore volume of different samplesis dominated by mesopores,followed by transitional pores and micropores, with the macroporesbeing the least abundant 7a. The c4.44.4.114 have shown that sandstone contains oil whenthe QGF-E intensity is higher than the 40 photometer count (pc). Inthis study, the normalized QGF-E intensity, in our samples, is ashigh as 2097291.5 pc, with an average normalized QGF-E intensity of585727.4 pc, which is much higher than sandstone and Qingshankou Fmshale (average 124151.53 pc),14 indicatingthat it has good oil content  contenthas a significant positive correlation with S1 and OSI, which indicates that the higher the value, the strongerthe fluid fluidity. This parameter can be used to divide the sweetsection  in the strong reducing environment where the water bodyis salty, the organic matter such as algae is relatively rich, andthe content of isoparaffins and naphthenes in the produced crude oilis relatively high;35 (2) the source rocksof Lucaogou Fm have a good organic matter type and early hydrocarbongeneration, forming large-scale low mature shale oil, with a highcontent of gum and asphaltene   6. During55.15.1.1S1 decreased significantly light oil sweet spots in the upper part ,which are characterized by high porosity, high content of macro- andmesopores, high OSI index (more than 200 mg/g·TOC), high S1, low organic matter content and low TOC content.There are excellent source rocks with high HI index (>600 mg/g·TOC)and high organic matter content (>4 μL/g) nearby, showingobviousthin source rock/reservoir interbedding. The effective thicknessesof the four sweet spots from shallow to deep are 2.0, 1.9, 3.1,and1.2 m. There are source rocks with good petroleum generation potentialin the lower part . The HI of samples withhigh TOC is also high , but the light oil content is low and the physicalproperties are poor  were poor and oil viscosity was high, resultingin a large amount of oil retention and pore pressure increase. Thereis no light oil sweet spot in the lower part  due to the high content of heavy components, low content offree oil, poor physical properties, and insufficient residual fluidpressure.Lian et al.5.2.238 Therefore, the overpressure may be the main driving forcefor the short-distance migration of oil.38Although the average porosity of the Lucaogou Fm reservoirin theJimusaer sag is 5.74%, which is higher than the corresponding porosityof the lower limit of the oil content , it is an effective reservoir. However, the overallperformance is still characterized by low porosity and low permeability(average permeability is 0.01 mD), which requires a certain initialpressure for oil migration. Previous studies have shown that the initialpressure of tight reservoirs in the study area is generally 0.16–0.37MPa, and the maximum migration and accumulation power that buoyancycan provide is about 0.088 MPa, which is much lower than the powerrequired for migration. However, the formation pressure of the Permianis generally higher than 30 MPa, and the pressure coefficient is generallygreater than 1.2, which is in the overpressure zone.39 verified thatunder-compaction is one of the important factors for the overpressureof the Lucaogou Fm in the study area by using the logging responsecharacteristics of the formation .In addition, due to the good quality of source rocks in the studyarea, petroleum generation pressurization is also considered to beone of the important mechanisms for overpressure formation.38 The thermal simulation experiment of hydrocarbongeneration shows that Lucaogou Fm has substantial oil generation potential(cumulative oil generation of 383.68 mg/g·TOC) and high efficiencyof oil drainage (oil drainage rate is as high as 69.21%) in the lowmaturity stage (Ro < 0.8%), which resultsin the formation of large-scale low maturity oil.40 However, due to the characteristics of high viscosity ofcrude oil, short expulsion time, large thickness of source rock, andtight reservoir before hydrocarbon generation, the generated oil willbe difficult to be expelled and can only migrate into reservoirs overa short distance. Continuous oil generation pressurization can beused as the driver of the short distance migration and accumulation.From oil generation simulation experiments on source rocks in limitedspace, the pressure of shale with a TOC value of 2.56% can be as highas 38 Mpa.41 Taking 4366 m as the boundary,the average TOC of the upper source rock is 3.56%, and that of thelower part is 4.74%. The generation pressurization intensity of thelower part is significantly higher than that of the upper part, andas a result, oil has a tendency to migrate from the bottom to thetop.The main causes of overpressure are diagenesis, under-compaction,and petroleum generation. With increasing diagenesis, smectite transformsinto illite and releases a large amount of interlayer water and adsorbedwater. However, the content of smectite and illite in the study areadid not change significantly with the increase in burial depth 1, and th42 In addition to overpressure as the driving force, goodreservoir physical properties are also necessary for accumulation.The above four light oil sweet spots have high porosity and macro+ mesopore contents  provides continuouspower and material supply for oil accumulation, thus forming large-scaleoil and gas resources.In general, the source rock of the sweet spots in the Ji-Xwellhas a good organic matter type and high petroleum generation potential.Under overpressure, light oils in the lower part have a tendency tomigrate upward. There are four light oil sweet spots in the upperpart. The oil generation and pressurization of shale lead to the accumulationof light oil in the adjacent reservoirs (sandstone and dolomite),forming a mode of “source-reservoir symbiosis” 15.42 In contents 12. There6Thesecond part of the first Mbr of Lucaogou Fm in Jimusaer sagcontains a set of fine-grained silty sand, mud, and carbonate sedimentsdeposited in a saline lake environment. The lithology can be dividedinto seven types, with complex mineral compositions and strong substantialheterogeneity.The overall oil saturation is relatively high,but the maturityof crude oil is low, and the overall oil quality is heavy, which ismainly controlled by the sedimentary environment and maturity of sourcerocks. Lithology and physical properties are the main controllingfactors affecting the oil content. The high-quality source rock isdolomitic mudstone with a high organic content, and the high-qualitylight oil reservoir lithology is argillaceous dolomite and sandstone.Based on the fractal inflection point of the mercury injection curve,the pore space of the shale oil reservoir is divided into four categories,in which the higher the content of macropores and mesopores, the weakerthe heterogeneity of the pore structure, and the better the oil contentin the reservoir.Taking the burial depth of 4366 m as the boundary,there are fourlight oil sweet spots in the upper part , and there are excellent source rocks with a high HI and a highorganic matter content near each sweet spot. The pressurization causedby petroleum generation, and the formation of overpressured areasas a result, will drive the light oil to accumulate in high-qualityreservoirs over short migration distances; there are source rockswith good petroleum generation potential in the lower part, but thecontent of heavy components is higher and the content of free oilis lower.


Long noncoding RNAs play vital roles in development and progression of lung cancers. Nuclear paraspeckle assembly transcript 1 (NEAT1) polymorphisms were reported to be closely related to lung cancer susceptibility. Recently, numerous studies have been performed to detect the association between NEAT1 polymorphisms and lung cancer susceptibility. However, their results were inconsistent and controversial. So, we carried out a meta-analysis aiming to define the association exactly.Appropriate studies were retrieved from searching Web of Science, PubMed, Scopus, and Google scholar databases, updated January 31, 2021. The pooled odds ratios with 95% confidence intervals were calculated to estimate the strength of the association between NEAT1 polymorphisms and lung cancer risk. All of the data were analyzed with Stata 16.0.The results of this meta-analysis will be submitted to a peer-reviewed journal for publication.This meta-analysis will summarize the relationship between NEAT1 polymorphism and lung cancer. Smoking is a confirmed environmental risk factor of lung cancer. Although smoking is closely associated with lung cancer, it is estimated that half of the new cases come from people who never smoked or quitted for many years. Other studies have displayed that about one-fourth of lung cancer cases are nonsmokers. In addition, epidemiological studies have revealed that, in addition to environmental factors, susceptibility to lung cancer is a key genetic factor.–6Lung cancer is the highest incidence of malignant tumor in the world. Studies have found that long noncoding RNA (lncRNA) can affect the growth and apoptosis of many kinds of cancer cells as well as the progression and metastasis of cancer.,9 A large number of studies have also proved that lncRNA is mal-expressed in a variety of tumors, including gastric cancer, liver cancer, colorectal cancer, bladder cancer, and so on.–13Noncoding RNA is a kind of RNA molecule whose length is more than 200nt and lacks the ability of protein coding.,15 Long non-coding RNA nuclear paraspeckle assembly transcript 1 (NEAT1) is a nuclear-enriched lncRNA, and about 3.7 kb located on chromosome 11q13.1. Previous studies have illustrated that NEAT1 has tumor marker significance in colorectal cancer, breast cancer, and other tumors.,18 Studies have demonstrated that single-nucleotide polymorphisms in NEAT1 are related to the occurrence of cancer.,20As the most common genetic variation, single-nucleotide polymorphism has attracted more and more attentions in cancer research. According to previous studies, it has been found that single nucleotide polymorphism can predict the risk and prognosis of cancer.At present, there are many studies on the relationship between NEAT1 polymorphism and lung cancer susceptibility, whereas the conclusions are very different. In this article, meta-analysis was conducted to systematically and quantitatively analyze the results of published case-control studies on the relationship between single nucleotide polymorphism of NEAT1 and the risk of lung cancer, so as to provide evidence-based medical evidence for lung cancer susceptibility.22.1The protocol of this review was registered in OSF (OSF registration number: DOI 10.17605/OSF.IO/JRSY6). It was reported to follow the statement guidelines of preferred reporting items for systematic reviews and meta-analyses protocol.2.2All studies were included based on the following criteria: case–control study and the evaluation of the association between NEAT1 polymorphisms and lung cancer susceptibility.2.3The exclusion criteria are as follows: duplication publication with the same population and no available data even contracted with authors.2.4Two investigators independently performed a systematically computerized search for English studies through PubMed, Google scholar, EmBase, and Web of Science databases up to January 31, 2021. The keywords applied are as follows: “long noncoding RNA nuclear paraspeckle assembly transcript 1, lncRNA NEAT1 or long non-coding NEAT1,” “polymorphisms, variants, or variation,” and “lung cancer, lung carcinoma, lung tumor, or lung neoplasm.” Furthermore, studies were identified through manual searches on reviews and retrieved studies. The search strategy for PubMed is illustrated in Table 2.52.5.1The 2 reviewers complete the screening process independently, and any differences are decided by a third reviewer. The screening process of the article includes reading the title, the abstract and the full text, so as to determine whether it meets the inclusion criteria. The researchers record the reasons to exclude each study in light of the preferred reporting items for systematic reviews and meta-analysis guidelines and report the screening results as well. The flowchart is exhibited in Figure 2.5.2Data were extracted by 2 independent investigators from included studies.P value for Hardy–Weinberg equilibrium (HWE) of controls. Meanwhile, we categorized ethnicity as White, African, or Asian.Divergence was solved by discussing every item. The collected information from each article was as follows: first author, publication year, region, cases and controls number, source of control, genotype frequencies, genotyping method, and 2.5.3 The quality score items included the representativeness of the cases, the source of the controls, genotyping examination, HWE in controls and association assessment. The NOS values arrange from 0 to 9. Studies with the score of 6 are considered to be of high quality.Two investigators independently evaluated the quality of the included articles on the basis of the Newcastle-Ottawa scale (NOS) that is adopted to evaluate the quality of observational studies.2.5.4The reason for the loss of data in the period of data screening and extraction is identified here. We would attempt to contact the authors if the data of potential studies are insufficient, missing, or vague. These studies would be excluded only if the data are not available through the method described above.2.5.5χ2 test, and P < .05 was seen as significant disequilibrium. Odds ratios (ORs) and the 95% confidence intervals (95% CIs) were calculated to evaluate the association of the NEAT1 polymorphism and lung cancer susceptibility. The pooled ORs were executed for homozygote comparison, dominant and recessive models, allele comparison and heterozygote comparison. The heterogeneity was calculated by performing χ2-based I2 test and Q test. The fixed-effect model (the Mantel-Haenszel method) was chosen when I2 value is <50%. When I2 is >50%, a random-effects model (DerSimonian and Laird method) was adopted. All of the statistical analyses were conducted by STATA 16.0 , and the P values were 2-sided.The HWE for control subjects of each study was evaluated by carrying out 2.5.6According to different ethnicity, genotyping method, and so on, we carried out subgroup analyses of the relationship between NEAT1 polymorphisms and the risk of lung cancer.2.5.7The eligible study was sequentially removed to perform the sensitivity analysis.2.5.8P < .05.,25Publication bias was assessed by Begg rank correlation and Egger linear regression. The publication bias was regarded to be statistically difference when 2.5.9The content of this article does not involve moral approval or ethical review and would be presented in print or at relevant conferences.3,27 Recent studies have shown that lncRNA plays an important role in tumor proliferation, metastasis, and angiogenesis, and is often involved in tumor regulation as a tumor suppressor gene.,29 At the same time, lncRNA has the feature of specific expression in tissue cells, and can be adopted to classify tumor subtypes or to predict the therapeutic effects of drugs.LncRNA is a kind of functional RNA molecules that are longer than 200nt, and most of them lack the function of coding protein.–34 The study also revealed that the single nucleotide polymorphism of NEAT1 gene is related to its expression. For example, rs2239895 polymorphism has enhancer activity, and can also upregulate the expression of NEAT1. In this article, meta-analysis was used to analyze the relationship between multiple mutation sites of NEAT1 and lung cancer susceptibility, which will provide a new idea for the diagnosis and treatment of lung cancer.Previous studies have confirmed that NEAT1 is abnormally expressed in many human malignant tumors, including leukemia, glioma, non-small cell lung cancer, ovarian cancer and breast cancer, and participates in the occurrence, metastasis, and prognosis of tumors.This study has the following shortcomings. First, meta-analysis itself is an observational study, so the lack of original experimental data limits further researches, such as the inability to carry out subgroup analysis of different genders and different types of lung cancer. It is impossible to analyze the interaction between two sites. Second, there may exist publication bias, because the literature with positive results is easier to publish. Third, some included loci in the literature are insufficient, so it cannot be further stratified analysis. In spite of this, this study still ensures the truthfulness and reliability of the research results from the following points. First of all, this article included a large number of cases and controls from different studies, and the literature was collected until January 2021, involving Asian and White races, which significantly increased the statistical testing efficiency. What's more, for the same population source or repeatedly published literature, only the larger or recent data results are included in the study, thus ensuring that there is no obvious selection bias.Conceptualization: Ming Li, Zhongbao Hu.Data curation: Zhongbao Hu, Jingsheng Chen.Formal analysis: Ming Li, Ping Meng.Funding acquisition: Ming Li.Investigation: Zhongbao Hu.Methodology: Ping Meng, Zhongbao Hu, Zhongbao Hu.Project administration: Ming Li.Resources: Jingsheng Chen.Software: Jingsheng Chen.Supervision: Jingsheng Chen, Ping Meng, Zhongbao Hu.Validation: Zhongbao Hu, Jingsheng Chen, Ping Meng.Visualization & software: Zhongbao Hu, Jingsheng Chen.Visualization: Ping Meng.Writing – original draft: Zhongbao Hu, Ming Li.Writing – review & editing: Zhongbao Hu, Ming Li.