The seroprevalence of measles and VZV was significantly higher among doctors and nursing assistants than among nurses and other HCWs (p < 0.001 in both). In conclusion, the seroprevalence of measles and VZV significantly decreased in younger HCWs. Additionally, monitoring the serostatus of measles and VZV and the immunization of susceptible HCWs are required to prepare and control infectious diseases in healthcare facilities.Measles and varicella still occur in the general population despite the widespread vaccination against them, and healthcare workers (HCWs) are still at risk of exposure to these diseases. Here, we evaluated the seroprevalence of measles and varicella-zoster virus (VZV) in HCWs and the trend of seroprevalence according to age, birth year, and occupational group. The serostatuses of measles and VZV of HCWs during new employee medical examinations between October 2015 and October 2021 were included. Thereafter, the trends of seroprevalence according to age, birth year, and occupational groups were evaluated. Overall, 2070 and 1827 HCWs were evaluated for VZV and measles serostatus, respectively. The seroprevalences of VZV and measles were 91% (1884/2070) and 70% (1284/1827), respectively. Younger HCWs had a significantly lower seroprevalence of measles ( Varicella and measles were major threats to public health because of their high transmission rates and complications ,2. The bIn a previous study that evaluated the seroprevalence of measles in Korea, the seroprevalence declined with time in the general adult population, particularly in those born between 1990 and 2001 . The serSeroprevalence of the varicella-zoster virus (VZV) was high and reached over 90% of the general adult population in Korea ,22. ReceIn this study, we evaluated the serostatuses of measles and VZV among HCWs, including doctors, nurses, nurse assistants, and other healthcare givers. The seroprevalence of measles and VZV and its trends were assessed according to birth year, age, and occupational groups.This study included HCWs who were examined for the serostatuses of measles and VZV during new employee medical checkups at Konkuk University Medical Center, a tertiary hospital in Korea, between October 2015 and October 2021. The seroprevalence of measles and VZV in HCWs was assessed according to their birth year, age, and occupational groups. Birth years were distributed in 5-year intervals; 1950–1954, 1955–1959, 1960–1964, 1965–1969, 1970–1974, 1974–1979, 1980–1984, 1984–1989, 1990–1994, and 1995–1999. Age groups were also distributed in 5-year intervals; 20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, and over 60. Age was calculated by the difference between the year of the test performed and the birth year. Additionally, the occupations of HCWs were grouped into doctors, nurses, nurse assistants, and other HCWs. The seroprevalence of VZV and measles was calculated as the seropositivity rate in each group. Thereafter, the changes in seroprevalence according to birth year and age groups and the comparison of seroprevalence between occupation groups were evaluated. The study was approved by the Institutional Review Board of the Konkuk University Medical Center (IRB No. 2022-08-069).Recently hired HCWs were examined for their immunity to measles, VZV, hepatitis B, and hepatitis C, and the presence of latent tuberculosis as a new employee medical checkup. Only immunity to hepatitis B was assessed when the first hospital originally opened. The evaluations of immunity to VZV and measles were included in the new employee medical checkups in March 2009 and March 2016, respectively. The serostatus of measles was evaluated in HCWs who did not prove to have immunity to measles by providing a certificate of two-dose vaccination or previous infection. VZV serostatus was assessed to determine VZV immunity. Vaccinations for susceptible infectious diseases were recommended to HCWs who were susceptible to measles, VZV, and hepatitis B.The serostatus of measles was evaluated using a chemiluminescent immunoassay . The test result was regarded as positive when the titer was equal to or above 16.5 AU/mL, negative when the titer was below 13.5 AU/mL, and indeterminate when the titer was between 13.5 and 16.5 AU/mL. The serostatus of VZV was evaluated by an enzyme-linked immunosorbent assay using a Chorus analyzer . Additionally, the result was considered positive when the titer was above 1.2, negative when the titer was below 0.8, and indeterminate when the titer was between 0.8 and 1.2. The serostatuses of measles and VZV were examined according to the manufacturer’s instructions ,24. Posip-value was < 0.05. In the comparison of seroprevalence between each occupation group , statistical significance was considered when the p-value was < 0.008 (according to Bonferroni’s method). Statistical analyses were conducted using SPSS , and graphs were obtained using PRISM-5 .The seroprevalence of VZV and measles was calculated among the overall population, birth year groups, age, and occupational groups. A correlation analysis was used to evaluate the correlation between the seroprevalence of VZV and measles according to birth year and age. Furthermore, a linear regression analysis was used to assess the trend of seroprevalence according to birth year and age. Comparison of the seroprevalence of measles and VZV between occupational groups was evaluated by Chi-square and Fisher’s exact tests. Moreover, comparisons of the seroprevalence between each occupational group were conducted. Statistical significance was considered when the In this study, overall, 2070 HCWs were examined for the serostatus of VZV, of which 1 (0.1%) person was born in 1950–1954, 4 (0.2%) in 1955–1959, 15 (0.7%) in 1960–1964, 29 (1.4%) in 1965–1969, 20 (1.0%) in 1970–1974, 28 (1.4%) in 1975–1979, 140 (6.8%) in 1980–1984, 350 (16.9%) in 1985–1989, 908 (43.9%) in 1990–1994, and 575 (27.8%) in 1995–1999. According to the age group, the population distribution was as follows: 699 (33.8%) in 20–24 years, 867 (41.9%) in 25–29 years, 309 (14.9%) in 30–34 years, 113 (5.5%) in 35–39 years, 17 (0.8%) in 40–44 years, 17 (0.8%) in 45–49 years, 29 (1.4%) in 50–54 years, 15 (0.7%) in 55–59 years, and 4 (0.2%) in over 60 years.Overall, 1827 HCWs were assessed for the serostatus of measles, of which 1 (0.1%) was born in 1950–1954, 4 (0.2%) in 1955–1959, 15 (0.8%) in 1960–1964, 29 (1.6%) in 1965–1969, 19 (1.0%) in 1970–1974, 27 (1.5%) in 1975–1979, 134 (7.3%) in 1980–1984, 330 (18.1%) in 1985–1989, 836 (45.8%) in 1990–1994, and 432 (23.7%) in 1995–1999. According to the age group, population distribution was as follows: 573 (31.4%) in 20–24 years, 780 (42.7%) in 25–29 years, 288 (15.8%) in 30–34 years, 106 (5.8%) in 35–39 years, 16 (0.9%) in 40–44 years, 16 (0.9%) in 45–49 years, 29 (1.6%) in 50–54 years, 15 (0.8%) in 55–59 years, and 4 (0.2%) in over 60 years.The seroprevalence of VZV was 91% , and the seroprevalence was 100% in HCWs who were born before 1965 . In mostThe seroprevalence of measles was 70% . The seroprevalence of the birth year groups was highest in 1950–1954 (100%) and lowest in 1975–1979 (63%) . In mostp < 0.001 in both). The seroprevalence of measles and VZV was significantly higher in doctors than in nurses (p = 0.002 for measles and p = 0.004 for VZV) or other HCWs (p = 0.005 for measles and p < 0.001 for VZV). Additionally, nurse assistants showed a higher seroprevalence of measles than nurses (p = 0.002) and other HCWs (p = 0.002). There were no significant differences between the seroprevalence of doctors and nurse assistants.The seroprevalence of VZV and measles was highest in nursing assistants (100% for VZV and 92% for measles) and lowest in nurses (91% for VZV and 68% for measles) and other HCWs (87% for VZV and 68% for measles) . The occp = 0.001) and age (p = 0.003). In the linear regression analysis, the seroprevalence of VZV significantly decreased in younger HCWs  (p = 0.04) rather than birth year (p = 0.07), and the seroprevalence of measles was reduced in younger HCWs. This trend was significant in the age group (p = 0.02) rather than in the birth year group (p = 0.06)  . Further = 0.06) .In this study, the seroprevalence of VZV was 91%. Although the seroprevalence of VZV was above 90% in most age groups, the seroprevalence in the younger age group was lower than 90%. The seroprevalence of VZV significantly decreased with age. Moreover, the seroprevalence of measles was 70%, which was lower than 90% in most age groups. The seroprevalence of measles also significantly decreased with younger age. Doctors and nurse assistants had significantly higher seroprevalence than nurses and other HCWs.The incidence of measles has decreased with measles vaccination . HoweverThe present study showed 91% seropositivity for VZV among the HCWs. Our finding was similar to previous studies that reported a seroprevalence of over 90% in Korean adults ,21,22,33The present study showed that the seroprevalence of measles was 70% and the seropositive rate was variable from 55% to 100% with age group. This variation in seropositive rate could result from the changes in vaccine strategy and the incidence of measles in Korea. Previous studies similarly reported the seroprevalence of measles from 73% to 93% among HCWs ,16,17,18The seroprevalence of measles and VZV was significantly different between occupational groups. In previous studies, the seroprevalence of measles and VZV were significantly different between the occupational groups ,46. HoweThe present study had some limitations. First, the effect of the NIH program on the seroprevalence of VZV among HCWs could not be evaluated. Therefore, continuous evaluation of seroprevalence among HCWs is required to assess the varicella vaccination effectiveness. Second, vaccination history was not evaluated. This study was conducted with new employee medical examinations; hence, we could not evaluate the vaccination history of HCWs. Considering that the vaccination rate of measles is >90% in the general population, measles vaccination history might have little effect on seroprevalence. Additionally, because we could not evaluate the varicella vaccination history, overestimation or underestimation of the seroprevalence of VZV could occur. However, the overall seroprevalence of VZV in this study was similar to that reported in previous studies. Third, because the present study was performed among HCWs, a selection bias could have influenced the results. Seroprevalence could be overestimated in HCWs compared with the general population. Fourth, the study was conducted at one medical center. Therefore, extrapolation to other geographical areas and healthcare facilities is limited. Fifth, the older age groups over the 50s were relatively small. Seroprevalence was evaluated during the new employee medical examination. Therefore, the older age groups were relatively smaller than the younger ones. However, the seroprevalence of measles and VZV significantly decreased in younger HCWs.The seroprevalence of measles and VZV among the HCWs was 70% and 91%, respectively. The seroprevalence of measles and VZV was significantly lower in the younger HCWs than the older ones. Additionally, nurses and other HCWs had a lower seroprevalence of measles and VZV than doctors and nursing assistants. Continuous monitoring of seroprevalence is required to modify the infection control strategy and prevent outbreaks of infectious diseases in healthcare facilities.


Sepsis is a leading cause of morbidity and mortality worldwide and is characterized by vascular leak. Treatment for sepsis, specifically intravenous fluids, may worsen deterioration in the context of vascular leak. We therefore sought to quantify vascular leak in sepsis patients to guide fluid resuscitation.We performed a retrospective cohort study of sepsis patients in four ICU databases in North America, Europe, and Asia. We developed an intuitive vascular leak index (VLI) and explored the relationship between VLI and in-hospital death and fluid balance using generalized additive models (GAM).Using a GAM, we found that increased VLI is associated with an increased risk of in-hospital death. Patients with a VLI in the highest quartile (Q4), across the four datasets, had a 1.61–2.31 times increased odds of dying in the hospital compared to patients with a VLI in the lowest quartile (Q1). VLI Q2 and Q3 were also associated with increased odds of dying. The relationship between VLI, treated as a continuous variable, and in-hospital death and fluid balance was statistically significant in the three datasets with large sample sizes. Specifically, we observed that as VLI increased, there was increase in the risk for in-hospital death and 36–84 h fluid balance.Our VLI identifies groups of patients who may be at higher risk for in-hospital death or for fluid accumulation. This relationship persisted in models developed to control for severity of illness and chronic comorbidities.The online version contains supplementary material available at 10.1186/s13054-022-03968-4. We created a novel vascular leak index (VLI) that identifies sepsis patients with higher risk for in-hospital death and fluid accumulation. Our VLI could be used to guide fluid resuscitation in sepsis patients.Sepsis is a leading cause of morbidity and mortality worldwide. In 2017, there were approximately 49 million sepsis cases worldwide, resulting in 11 million related deaths . Sepsis Expert guidelines recommend infusion of intravenous (IV) fluids to increase venous return, cardiac stroke volume, cardiac output, and ultimately tissue perfusion . ClinicaMeasuring vascular leakage of fluid and protein requires special equipment that may not be widely available. Some investigators have proposed that the degree of vascular leak can be inferred by measuring hematocrit levels as IV fluids are infused. Hematocrit represents the concentration of hemoglobin, a protein too large to leak out of the vasculature. If infused fluid remains within the vasculature, hematocrit levels decline, as plasma volume increases and hemodilution occurs. If fluid leaks from the vasculature, the hematocrit should decline more slowly or even increase.The goal of this investigation was to formulate an easily calculated vascular leak index (VLI) that clinicians can use to estimate prognosis and guide care. We hypothesized that increasing VLI would be associated with a more positive fluid balance and an increased risk for death. We aimed to refine existing definitions of VLI by factoring in other key characteristics and clinical measurements , 15. We We conducted a retrospective analysis of patients from the eICU, MIMIC-III, AmsterdamUMCdb, and SNUH databases –20. DetaOur study population includes patients diagnosed with sepsis by ICD9 and Angus criteria in the eICU-CRD and MIMIC-III databases . In AmstWe developed an intuitive equation for VLI based on relevant variables, including hematocrit levels at two time points during ICU care and net volume of fluid administered. We reasoned that the relationship between the volume of fluid infused and the change in hematocrit would yield information about how much fluid remained in or escaped from the vascular space. Specifically, the change in the hematocrit divided by the net fluid balance would quantify vascular leak. To normalize for differences among patients’ blood volume, we divided the fluid volume by each patient’s body surface area, as suggested by Nadler and colleagues Eq.  22]. We. We22]. Vascular leak indexInitial hematocrit values were measured at study baseline (0 h), including the range during the previous 12 h when the patient may have been admitted to the emergency department through the following 18 h. The first hematocrit during this timeframe was considered the initial value. Final hematocrit levels were calculated as the average among hematocrit values measured between 18 and 36 h. The volume of total fluids administered between the previous 6 h prior to ICU admission and 36 h was recorded. Urine output was recorded, and the net fluid balance was calculated as total fluid input minus urine output.Patient outcomes included in-hospital death and fluid balance from 36 to 84 h after ICU admission, given the average ICU length of stay is 3.3 days or 79 h . For theIn addition to our variables of interest, we identified potential confounders that would account for a patients’ physical characteristics, such as age and sex. We accounted to disease severity and comorbidity burden with the Acute Physiology and Chronic Health Evaluation (APACHE) IV score and the Charlson Comorbidity Index (CCI) in eICU, the Oxford Acute Severity of Illness Score (OASIS) and the Elixhauser Comorbidity Score in MIMIC III, APACHE II in AmsterdamUMC, and APACHE II in SNUH. Comorbidity information was not available for the AmsterdamUMC and SNUH databases.After developing our VLI, we trained a Generalized Additive Model (GAM) to determine the relationship between VLI and in-hospital death and 36–84 h fluid balance. We were interested in the nonlinear relationship between VLI and the two outcomes. Because of this, a spline term was introduced for VLI. In addition, we created quartiles of VLI as the explanatory variable.In our models, we controlled for covariates, specifically severity of illness, chronic comorbidity (if available), age, and biological sex. The relationship between VLI and the outcome variables as determined by the GAM is visualized across all values of VLI, and the statistical significance of the relationship is determined by the ANOVA test.All analyses were done in R 3.6.1. Code for our research can be found on our Github repository .In Table The demographics, clinical characteristics, and outcomes for the patients in the four databases are presented in Table The final distribution of VLI for each database for the in-hospital death cohort is presented in Additional file Using our GAM and analyzing VLI as quartiles, our results indicate that increasing VLI is associated with increased risk of in-hospital death in a dose-dependent manner. Patients in VLI Q4 had approximately 2.31 [CI 1.71–3.12], 1.61 [CI 1.26–2.05], and 2.13 [CI 1.42–3.20] increased odds of dying in the hospital compared to patients in VLI Q1 for eICU, MIMIC, and Amsterdam, respectively. VLI Q2 and Q3 were also associated with increased odds of dying , MIMIC (p = 0.004), and Amsterdam (p = 0.002).Using our GAM and treating VLI as a continuous variable, we observe that in-hospital death changes with different values of VLI Fig. . There iSee Additional file p < 0.001), MIMIC (p < 0.001), Amsterdam (p = 0.043).Using our GAM and treating VLI as a continuous variable, we observe that 36–84 h fluid balance changes with different values of VLI Fig. . Again, p < 0.001), 685 ml , and 528 ml  increased 36–84 h fluid balance compared to patients in VLI Q1 for eICU, MIMIC, and Amsterdam, respectively. Patients in VLI Q3 had approximately 800 ml , 769 ml , and 572 ml  increased 36–84 h fluid balance compared to patients in VLI Q1 for eICU, MIMIC, and Amsterdam, respectively.Treating VLI as quartiles, we observe that patients in VLI Q4 had approximately 1131 ml  as wellWe would also like to emphasize that we developed our VLI a priori based on physiological principles, not using regression models to identify variables that might contribute to risk. We used each independent data set to test whether there are associations between our VLI and the outcomes of interest. The fact that we saw similar associations in four independent data sets supports the generalizability of our findings despite creating models on each dataset separately.Limitations common to studies that use electronic medical record databases include mistakes in charting data that must be input manually. Errors in collecting fluid data are especially prevalent given the amount of different fluid inputs that a patient receives and the difficulty of accurately measuring urine output.In addition, we found that most patients have a negative VLI. That is, the hematocrit decreased over time as they received IV fluids. In some patients, however, we observed an increase in hematocrit over the study period, even as the patients received IV fluids. Because we sought to exclude patients who received blood component transfusions, the reasons for the observed increase in hematocrit are uncertain. Due to the nature of our equation, patients with a rising hematocrit have the highest VLI and are classified in VLI Q4. The biological plausibility of placing these patients in the highest risk group is unclear. We believe that including patients with a rising hematocrit is one reason why we observe a high variability between VLI and the two outcomes of interest at high VLI values and why, in the AmsterdamUMC cohort, we observe decreasing mortality and decreasing 36–84 h fluid balance at the higher range of VLI.Also, defining sepsis is particularly difficult, especially in a retrospective study when the diagnosis of sepsis may not be certain. In eICU, AmsterdamUMC, and MIMIC, we use relatively inclusive definitions, while in SNUH, we were restricted to identifying sepsis in patients who received that diagnosis at admission.A further limitation is the nature of our intuitive equation. We reasoned from physiological principles and attempted to normalize by using the body surface area as a surrogate for circulating blood volume. We recognize that body surface area may be an unreliable surrogate . NeverthIn the different hospitals that represent these four databases, fluid administration practices were quite different. In addition, depending on fluid administration practices, 36–84 h fluid balance and death can be significantly dependent on fluid balance in the first 36 h. This makes it difficult to cleanly describe the relationship between VLI and 36–84 h fluid balance.Finally, while the patient population size was sufficiently large in the eICU, MIMIC, and AmsterdamUMC databases, the SNUH database had a very small final cohort size. The relationships seen in the other three datasets were generally maintained in the SNUH dataset, but the low sample size led to high variability in estimates.Using a VLI derived from changes in hematocrit and net fluid balance within the first 36 h of a patient’s ICU care, we were able to estimate vascular leak and identify a population with a higher risk for dying and increased 36–84 h fluid balance in the hospital using a GAM that controlled for disease severity and chronic comorbidities. Future studies should validate our VLI and test whether using our VLI to guide therapy may result in patient-centered benefit. Other future studies should examine other outcomes, such as hospital length of stay, risk for new renal replacement therapy or mechanical ventilation, and others.Additional file 1. Supplementary Material.


Fluid therapy for sepsis patients has always been a problem that puzzles clinicians, that is, knowing when patients need fluid infusion and when they need negative fluid balance. Different clinicians may have different judgment criteria and make different decisions. Recently, studies have suggested that different fluid treatment strategies can cause different clinical outcomes. This study is intended to establish and verify a model for judging the direction of fluid therapy based on machine learning.This study included 2705 sepsis patients from the Peking Union Medical College Hospital Intensive Care Medical Information System and Database (PICMISD) from January 2016 to April 2020. The training set and test set (January 2016 to June 2019) were randomly divided. Twenty-seven features were extracted for modeling, including 25 state features , 1 action feature  and 1 outcome feature . SARSA was used to learn the data rules of the training set. Deep Q-learning (DQN) was used to learn the relationship between states and actions of the training set and predict the next balance. A double-robust estimator was used to evaluate the average expected reward of the test set in the deep Q-learning model. Lastly, we verified the difference between the predicted fluid therapy model and the actual treatment for the patient's prognoses, with sepsis patient data from July 2019 to April 2020 as the validation set.Q calculated by SARSA from the training set, the lower the mortality rate. Deep Q-learning indicates that both fluid balance differences that are too high and too low show an increase in mortality. The more consistent the fluid balance prediction with the real result, the lower the mortality rate. The smaller the difference between the prediction and the reality, the lower the mortality rate. The double-robust estimator shows that the model has satisfactory stability. The validation set indicates that the mortality rate of patients in the “predicted negative fluid balance and actual negative fluid balance” subgroup was the lowest, which was statistically significant, indicating that the model can be used for clinical verification.The training set and test set were extracted from the same database, and the distribution of liquid balance was similar. Actions were divided into five intervals corresponding to 0–20, 20–40, 40–60, 60–80, and 80–100% percentiles of fluid balance. The higher the reward of We used reinforcement learning to propose a possible prediction model for guiding the direction of fluid therapy for sepsis patients in the ICU. This model may accurately predict the best direction for fluid therapy, thereby improving patient prognosis. Fluid resuscitation is the basic treatment for sepsis. However, in recent years, clinicians have found that an inappropriate infusion of large amounts of fluid may cause volume overload, which has become an independent risk factor for disability and death during critical illness –4. In thPatients who were diagnosed with sepsis between January 2016 and April 2020 at the Department of Intensive Care, Peking Union Medical College Hospital, were included in this study. Informed consent was given by patients  before any data extraction. Patients were excluded from this study if they met any of the following criteria: (1) they were younger than 18 years old or (2) they were admitted to the ICU for fewer than 24 h.The initial timing of fluid resuscitation is when patients are diagnosed as “sepsis”. Specifically, definition of sepsis is the time when both “infection is diagnosed” and “new onset of ΔSOFA ≥ 2” are reached . InfectiData were extracted from the Peking Union Medical College Hospital Intensive Care Medical Information System and Database (PICMISD). The parameters extracted from the database included demographics, vital sign data collected by bedside monitoring, laboratory examination data, blood gas analysis, microbiological examination results, antibiotic usage and total fluid balance. Notably, data de-identification was performed before any further analysis.The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. All the experimental protocols were approved by the Institutional Research and Ethics Committee of Peking Union Medical College Hospital, which approved this study for human subjects (No. SK-1241).A total of 2,705 patients were included based on sepsis 3.0. The average ICU time was 4.3-day, as calculated by PICMISD. We included patient data over each 6 h period up to 108 h (4.5 days), for 18 periods in total. For patients who transferred out of the ICU or died within 108 h, the last period was their last record in the ICU. The values of each feature are the average value for each period. The final outcome was patient death or survival over the last period, that is ICU survival or death. Death includes clinical death and the withdrawal of treatment without any further action. Each 6 h is identified as a block, which is abbreviated as “Bloc”. Because the laboratory features do not need to be measured frequently, the missing values were forward-filled. Missing values of other features with <30% missing rate were filled by KNN. Twenty-seven features were used for modeling, including 25 state features , one action feature , and one outcome feature. We included 2,443 sepsis patients, with 2,095 survivors and 348 non-survivors. Each group of data includes all 27 features for one period for one patient. Each patient could contribute 18 groups of data at most depending on their ICU time. There were 31,425 groups of data generated from 2,443 patients  to iteratively improve the behavior of the learning agent. Q-Learning technique is an off-Policy technique and uses the greedy approach to learn the Q-value. SARSA technique, on the other hand, is an On Policy and uses the action performed by the current policy to learn the Q-value. DQN model is based on Q-learning, which we use to uncover the relationship between states and actions, obtain new knowledge from existing data and find the best treatment plan to predict the reasonable range of fluid balance.The purpose of reinforcement learning is to obtain an optimal policy for a specific problem to maximize the reward obtained under a given strategy. A strategy is mapped from state to action, which can determine what behavior to choose under a certain state and then enter the next state. The transition in the state is equivalent to adding a reward to the Markov decision process. Hence, the next state is related not only to the current state but also to the current action. Every decision consists of a status, behavior and reward. Given that the prediction is the interval of fluid balance, which can be divided into five intervals, we used the value iteration method of reinforcement learning.In this research, each data point is a group of states, which includes 25 features except the balance and outcome. The action refers to how much liquid should be given under a certain state, which is divided into five groups by 0–20, 20–40, 40–60, 60–80, and 80–100% of the percentiles of fluid balance. Each group of actions and the corresponding balance is shown in Q, with s being the current state and a being the performing action. Q is also the q-value performing action a in state s. All Q are initialized as 0, and Q′ is updated by performing different actions under different states and obtaining rewards from different next states. Then, the model can learn the possibilities of different Q under a certain state and choose the action with the highest possibility as the next action.All the patients were divided randomly 8:2 into a training set and test set, corresponding to 2,095  in the training data and 348 patients  in the test data. We needed to evaluate the expected reward when performing an action in a certain state; the higher the expected reward is, the better the effect of performing this action. The reward is called Q′: SARSA and Q-learning  in SARSA is as follows:The formula for updating the Q′ of Q-learning is as follows:The formula for updating the where r is the reward of the current action and α and γ are the parameters of 0–1.Q′, SARSA chooses a′ from all possible next actions to the next state s′ and obtains Q′. Then, it performs a′ in state s to state s′. Q′ will be updated in the next iteration from one next action in state s′. Q-learning updates Q with the highest Q′ in state s, but the next action will be chosen in another way in the next iteration rather than using the a′ of Q′. In general, the a′ in state s is chosen randomly; in this case, we could find out how much balance (a1) the patients have in state s1, then enter state s2, but if the action is random (such as a2), and all patients in state s1 did not have the balance a2 in the existing data, we would not know what the next state could be. Thus, the next state should be chosen from the given sequence of data. The updated strategy for Q with SARSA is exactly the same as the actual implemented strategy, which is called on-policy, while inconsistent with Q-learning, which is called off-policy. SARSA is aimed at learning the characteristics of the original data, and Q-learning tends to discover new strategies  is the expected reward. The relationship between the expected reward and mortality could be acquired by calculating the mortality rate of each data point in Q. Ideally, the higher the expected reward is, the lower the mortality rate. We used the training set to build the SARSA model. Since the state cannot be exhaustive, we used a function  as the state to build a reinforcement learning model. The neural network consists of 1 input layer, two hidden layers, and one output layer. The input layer consists of 25 features (25 nodes). Each hidden layer has 128 hidden nodes. The output layer is the probability of Q corresponding to the five action categories, and softmax is used to select the highest probability action as the final output.First, we used SARSA to learn the characteristics of the original data to then obtain the relationship between reward and mortality to evaluate whether the rewards were reasonable. w and biases b. The weights are different in different nodes, and the biases are the same in the same layer and different in different layers. w and b are initialized as randomly generated decimals of a normal distribution with a mean value of 0 and a standard deviation of 1, generally within the interval . Due to the size of the training set, data are usually trained in small batches in a fixed-size sample that is randomly selected from all training data for each iteration. The sample size is called the batch size, and the batch size of this project is 32. First, the network receives the input and calculates using the received input and parameters, comparing the estimated output with the real output, it obtains the mean square error, and then it updates the parameters of each node according to the error. The learning rate for updating the parameters is 0.0001 to ensure that the amplitude of each update parameter will not be too large so that the current parameters can gradually approach the best parameters. Normally, the error will keep decreasing during training, and the decrease will be fast and then slow. When the error becomes stable as the training times increase, the training can be stopped to avoid overfitting.Each node of the neural network represents a function and includes weights Q. In the project, there are five actions, a1, a2, a3, a4, a5. Suppose the states are s1,s2,s3.... Each data point includes a state and action group. The updating order is the same as the data order. As shown in s1, the action to be performed is a2, and we update Q. With first input s1, we could have five Q values of five actions, then we could obtain s2 as the next state and a1 as the next action, using Q to update Q. Then, we would update Q… in the same way until the end of the iterations.Next, we used deep Q-learning (DQN) based on Q-learning to uncover the relationship between states and actions, obtain new knowledge from existing data and find the best treatment plan to predict the reasonable range of fluid balance. We used the training set to train the model and the test set to make predictions on the trained model.Q. The updating order of Q is also the same as the data order; unlike SARSA, DQN uses the highest Q among five Q to update Q. Suppose the highest Q is Q, and we use Q to update Q.Q with DQN and Q-learning is the same. Both use the max method. However, there will be an overestimation problem. If the data are noisy, the best Q is not the largest Q. There may be biases after each iteration, and the biases will approach Q with greater values  with DDQN is as follows:To solve the problem, we use double deep Q-learning (DDQN). The method of updating The method of calculating losses with DQN and DDQN is shown in θ is the parameter of the neural network; the method of obtaining Q is called selection, and the calculation loss is called an evaluation. DDQN implements the two methods with two Q networks, avoiding the risk of using the same Q network.We define two identical structured Q networks, the primary QN and target QN, train with the primary QN and evaluate with the target QN . The calQ in st+1, where Q is called q1;(1) Use the primary QN to select the action with the highest Q of the action, and the evaluated Q is called q2;(2) Use target QN to evaluate the q2 and q1 as the loss, update the parameters of the primary QN with back propagation, and do not update the parameters of target QN;(3) Set the difference between (4) Repeat (1) (2) (3) until convergence.The algorithm evaluation method compares the original average reward and the average reward calculated by the model on the test set. If the average reward calculated by the model is higher than that of the original test set, we can argue that the model has an effect on optimizing treatments. However, because the model is built on the training set, we cannot ensure that the distribution (probability of occurrence) of the test set is the same as that of the training set, and it is inaccurate to use the mean prediction rewards for comparison. To ensure that the two groups of data are unbiased, the inverse probability score (IPS) is generally used for approximating the test set to the training set distribution.The formula of the IPS is as follows:i(xi) is the probability of an event occurring. If the probability on the test set is lower than that of the training set, as the denominator, r will be multiplied by a multiple greater than 1, which is the inverse. If the probability of an event occurring is correct, the test set will be unbiased. However, there are two problems. First, we cannot ensure that the probability is completely correct. Second, the variance will be too large if the probability is very small.where A is whether an event occurs or not, r is the reward, and πm(xi) for prediction, and the formula is as follows:We can also use regression-based model where A indicates whether AI policy is consistent with clinical policy, 1 indicates consistency and 0 indicates inconsistency.A = 1, (1 − Ai) is 0, and according to the formula, the original reward will be used. If A = 0 and Airi is 0, the predicted reward will be used. Similarly, if the model is correct, the result will be unbiased.If A double-robust estimator is the combination of IPS and a regression model. The formula is as follows:OR:m(xi) is the model, which is used for predicting the reward. If the model is correct, according to formula 4, the model is able to predict ri, and if ri − m(xi) = 0, where  robust) –17.Patient data from July 2019 to April 2020 were extracted from PICMISD as the validation set. We compared the predictions and actual clinical conditions and prognoses of four different types of negative fluid balance and fluid infusion in the validation set. The preprocessing method of the validation set is the same as that used for the data collection and cleaning strategy in Section 2. Lastly, the validation set included 399 sepsis patients (representing 5269 time-blocks) were used for verification, with 357 survivors (including 4677 time-blocks), and 42 non-survivors (including 592 time-blocks). We divided the predicted and clinical 5-group fluid balance into two groups based on the total amount of fluid . The previous categories 0, 1, and 2 correspond to the new category 0, indicating negative fluid balance , and categories 3 and 4 correspond to the new category 1, indicating fluid infusion . In addition, the actual negative fluid balance and fluid infusion were determined according to the actual clinical PICMISD records. We calculated the morality of the four combinations of prediction and clinical results. The mortality rate is the lowest when the predicted fluid management strategy is the same as the actual strategy.t-test or the Wilcoxon rank sum test was used to compare continuous variables. The categorical data were compared using the chi-square test or Fisher's exact test. The Kruskal-Wallis test was used to compare differences in continuous parametric variables with abnormal distributions. Differences in the variables between the groups were considered statistically significant at the a p level of <0.01.All statistical analyses were performed using SPSS Statistics for Windows  and R 3.4.3. Student's p-values of all the features are greater than 0.01, which means that there is no significant difference between the two datasets.As shown in Q. Each training dataset includes a group of states and actions Q] of the original test set, and the average predicted reward was calculated by the Q-learning model. The result is shown in A double-robust estimator was used to calculate the average expected reward [As shown in Our study indicates that the use of reinforcement learning methods can clearly predict a patient's future liquid treatment strategy. To our knowledge, clinical decision-making is a process that usually not gives immediate feedback. A certain treatment strategy may improve or worsen the state of illness. However, it does not mean the treatment prior to change of the illness is right or wrong. In clinical practice, a series of treatment procedures work together to accomplish the therapeutic effect. Hence, clinical decision-making in ICU is not a simple static classification problem. Instead, it is a dynamic process which we should learn through environmental rewards and punishments. Under this circumstance, reinforcement learning should be used to maximize rewards or achieve specific goals through learning strategies in the interactive process of the environment. The SARSA model can be used to simulate the equation between expected mortality and actual mortality during the liquid treatment process. The Q-learning model shows that as the model prediction and actual intake and output become closer, the mortality rate decreases. Additionally, if the intake and output are too high or too low which caused abnormal fluid imbalance, the mortality rate would be higher. Patients with higher positive fluid imbalance may have higher mortality than those with a higher negative fluid imbalance. We used a double-robust estimator to calculate the average expected reward of the test set in the Q-learning model after training 30,000 times as the final prediction model. Using a validation data set, the results suggest that if the model predicts that the patient should be dehydrated while the patient is dehydrated under actual treatment, the fatality rate is significantly lower compared with other circumstances. In particular, these data are from the first 108 h of entering the ICU ward, which suggests that the resuscitation is not a positive mass fluid resuscitation. This is worth thinking about for the clinicians.p < 0.001) (Fluid overload is related to the prognosis of severely ill patients with septic shock and/or acute respiratory distress syndrome (ARDS). Excessive fluid load may lead to a vicious cycle in which interstitial edema causes organ dysfunction, leading to fluid accumulation, organ edema and dysfunction. All the potentially harmful consequences of fluid overload in different end-organ systems have an impact on patient morbidity and mortality. Therefore, although infusion is advocated in early resuscitation strategies, the side effects of inappropriate or excessive infusion are increasingly recognized by practitioners. Fluid therapy can be considered a double-edged sword. In 2000, a retrospective cohort study conducted by Alsousand et al. indicated that patients with a negative fluid balance for at least one day during the first 72 h of septic shock had a better prognosis  . In 2006< 0.001) . Since 2< 0.001) . A recenThe average infusion volume of patients with severe infection and septic shock on the first day of admission to the ICU is lower than recommended by the Surviving Sepsis Campaign bundle. An infusion of more than 5 L of fluid on the first day of admission to the ICU is associated with a significant increase in the risk of death and a significant increase in hospitalization costs . VincentHowever, we should never forget avoiding excessive negative fluid balance during treatment. Our goal for negative fluid balance is to remove excess volume in the interstitial spaces. However, the volume in the circulation must be removed first. When the interstitial fluid resorption rate (plasma refilling rate) is sufficient to prevent hypovolemia, hypotension does not occur. Currently, we do not completely know the lowest fluid resorption rate that may prevent hypotension. Studies have shown that in patients with severe negative fluid balance, increased fluid intake and urine output are related to a decrease in hospital mortality. However, achieving a more negative fluid balance compared to a mild fluid balance is not associated with reduced mortality . In addiSeveral limitations should be mentioned in this study. This study uses a single-center database. The sample size and the treatment stereotype of the treatment center may weaken the universality of the model. In particular, the weights of 18 blocks for each patient were equal in this study. At present, a method to solve the time series problem has been presented , 27. FurThis study proves that the reality of fluid therapy for patients with sepsis in the early stage of ICU admission is that a large amount of fluid infusion may not be required. If it can be converted to the negative fluid balance resuscitation phase as soon as possible, the patient's prognosis is better. Reinforcement learning methods were used to propose a possible predictive model for guiding the fluid therapy of patients with sepsis in ICUs.Our study presents a methodological model for fluid therapy. It is believed that machine learning will ultimately assist in clinical decision-making regarding the fluid therapy of critically ill patients in the future.slx77@163.com.The data analyzed in this study is subject to the following licenses/restrictions: This data was collected by Peking Union Medical College Hospital Intensive Care Medical Information System and Database (PICMISD), which is a protected database. If needed, we would like to disclose the data after the permission of Ethics Committee of Peking Union Medical College Hospital. Requests to access these datasets should be directed to LS, The studies involving human participants were reviewed and approved by Institutional Research and Ethics Committee of Peking Union Medical College Hospital. The patients/participants provided their written informed consent to participate in this study.YLo, WZ, and BD takes responsibility for the integrity of the work as a whole. LS is responsible for the study design and conception and drafted the manuscript. YLi, MS, and SZ were responsible for the study design and conception and for the data management and statistical analysis. LS, SL, XZ, and LW drafted the manuscript. All authors revised the manuscript for content. All authors contributed to the article and approved the submitted version.This study was funded by CAMS Innovation Fund for Medical Sciences (CIFMS) from Chinese Academy of Medical Sciences (No. 2021-I2M-1-062 & 056), National Key R&D Program from Ministry of Science and Technology of the People's Republic of China (No. 2021YFC2500800), Beijing Nova Program from Beijing Municipal Science and Technology Commission (No. Z201100006820126), China Health Information and Health Care Big Data Association Severe Infection Analgesia and Sedation Big Data Special Fund (No. Z-2019-1-001), China International Medical Exchange Foundation Special Fund for Young and Middle-Aged Medical Research (No. Z-2018-35-1902), Excellence Program of Key Clinical Specialty of Beijing for critical care medicine in 2020 (ZK128001), and Beijing Municipal Science and Technology Commission (No. Z201100005520051).LS, SL, XZ, LW, WZ, and YLo were employed by Peking Union Medical College Hospital. YLi, MS, and SZ were employed by DHC Software Co., Ltd. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


CpG methylation levels can help to explain inter-individual differences in phenotypic traits. Few studies have explored whether identifying probe subsets based on their biological and statistical properties can maximise predictions whilst minimising array content. Variance component analyses and penalised regression (epigenetic predictors) were used to test the influence of (i) the number of probes considered, (ii) mean probe variability and (iii) methylation QTL status on the variance captured in eighteen traits by blood DNA methylation. Training and test samples comprised ≤ 4450 and ≤ 2578 unrelated individuals from Generation Scotland, respectively.As the number of probes under consideration decreased, so too did the estimates from variance components and prediction analyses. Methylation QTL status and mean probe variability did not influence variance components. However, relative effect sizes were 15% larger for epigenetic predictors based on probes with known or reported methylation QTLs compared to probes without reported methylation QTLs. Relative effect sizes were 45% larger for predictors based on probes with mean Beta-values between 10 and 90% compared to those based on hypo- or hypermethylated probes .Arrays with fewer probes could reduce costs, leading to increased sample sizes for analyses. Our results show that reducing array content can restrict prediction metrics and careful attention must be given to the biological and distribution properties of CpG probes in array content selection.The online version contains supplementary material available at 10.1186/s13148-022-01320-9. DNA methylation (DNAm) involves the addition of methyl groups to the fifth carbon of cytosine bases, typically in the context of cytosine-guanine dinucleotides (CpG sites). There are approximately 28 million CpG sites across the human genome , 2, of wThere are two primary methods to quantify the amount of methylation at CpG sites interrogated by Infinium probes. First, the Beta-value  is a ratio of the methylated probe intensity to the overall measured intensity (sum of methylated and unmethylated probe intensities) , 7. The Illumina DNAm data are routinely utilised in health outcomes research. First, the arrays are employed in association studies to uncover individual genomic loci associated with disease states and other phenotypes . Second,Genetic, demographic and environmental factors contribute to inter-individual variability in CpG methylation . Common Probes with high inter-individual variation in DNA methylation might be more informative for capturing variance in human traits compared to those that are invariant . Here, we tested the hypothesis that invariant probes do not influence the amount of variance in phenotypes captured by Illumina array content. We utilised blood DNAm data and eighteen phenotypes from 4450 unrelated volunteers in the population-based cohort Generation Scotland as our training sample , 27. We We used two methods to investigate how the number of probes considered in a probe set and how their distribution properties influenced the amount of phenotypic variance captured by DNAm. First, we estimated the amount of phenotypic variation captured by DNAm in the training sample (reflecting within-sample trait variance). For this, we used OmicS-data-based complex trait analysis (OSCA) software in which the correlation structure among all input probes is used to create an omic-data-based relationship matrix (ORM). The ORM is then used to estimate variance components through the restricted maximum likelihood method (REML) . In esseP < 3.6 × 10–8). By comparing results from the primary and secondary probe sets, we were able to test the influence of (i) the number of probes considered, (ii) mean probe variability and (iii) methylation QTL status on the variance captured in eighteen traits by blood DNA methylation. Further, we compared results from these probe sets against those from randomly sampled sets of probes of equal size in order to determine whether observed estimates were significantly different from those expected by chance.We compared results from the five primary sets of probes to test our primary hypothesis, and these probe sets had decreasing numbers of probes and increasing mean variabilities. In further analyses, we also considered secondary subsets of probes with (i) an mQTL , (ii) hypo- or hypermethylated probes  and (iii) genome-wide significant EWAS Catalog probes  and four subsets of probes with decreasing sizes and increasing mean variabilities  ranged from 23.7% (standard error (se) = 6.0%) for blood potassium levels to 79.6% (se = 2.1%) for smoking pack years . Figure The four remaining probe sets containing 115,746, 50,000, 20,000 and 10,000 probes, on average, captured 47.9%, 40.6%, 30.4% and 21.9% of phenotypic variance across seventeen traits, excluding chronological age  captured between 0.74%  and 46.0% (smoking pack years) of trait variance in the test sample  captured 6.7%, 6.6%, 5.6% and 5.0% of phenotypic variation. The four traits with the highest incremental R2 estimates were comparable to maximal R2 estimates from the literature achieved with similar, linear DNAm-based predictors. These analyses are distinct from the earlier variance component analyses and reflect the performance of DNAm-based predictors in samples external to those in which they were developed . Subsets that contained 115,746, 50,000 and 20,000 probes also captured 100% of the variance. The subset containing the 10,000 most variable non-mQTL probes captured only 92.1%  were 87.4%, 87.7%, 85.7% and 83.9%, respectively . The RP = 0.001; Additional file We tested whether the most variable set of probes, i.e. the 10,000 most variable non-mQTL probes, were over or under-represented for certain genomic features. We compared genomic annotations from this subset to annotations from 1000 sub-sampled sets of 10,000 probes, which were drawn from all available non-mQTL probes . Highly variable probes were enriched in intergenic sites, 5’UTR regions and sites lying 200–1,500 bases upstream from a transcription start site (range of fold enrichment (FE) = , FDR-adjusted P = 0.001). They were also significantly under-represented within 3’UTR regions and gene bodies , (ii) probes with a known mQTL and mean Beta-value between 10 and 90% and (iii) probes with mean Beta-value ≤ 10% or ≥ 90%, that is, hypo- or hypermethylated probes (containing both mQTL and non-mQTL probes). The latter two classes are shown as the excluded probes in Fig. We performed further secondary analyses to determine the relative predictive capacities of four classes of probes. The first three classes were: (i) probes Across all classes, variance estimates decreased with the number of probes under consideration Table . All proprobe = 393,654). The 20,000 and 10,000 most variable EWAS Catalog probes showed estimates that were 91.5% and 85.3% of the magnitude of those from all ‘available probes’  captured as much variance as those based on ‘all available probes’ , and the 115,746 most variable of these probes, also captured as much phenotypic variance as predictors based on ‘all available probes’  were on average 15% larger for probes with mQTLs versus those without GoDMC mQTLs. Relative effect sizes were also approximately 45% greater for probes with mean Beta-values between 10 and 90% when compared to hypo- or hypermethylated probes with mean Beta-values ≤ 10% or ≥ 90%  and that many of the traits considered in this study feature in the EWAS Catalog. Furthermore, traits with strong epigenetic correlates were the most robust to changes in probe classification or the number of probes considered. For instance, REML suggested that 20,000 probes were sufficient to capture 100% of inter-individual variation in chronological age. However, only 90% of the variance in age could be explained by subsets containing 10,000 probes. Previously, it has been shown that 100% of the variance in chronological age is captured by DNAm in Generation Scotland and the Systems Genomic of Parkinson’s Disease consortium [2 estimates (~ 90%) are in line with, albeit lower than, those from existing epigenetic age indices, which employ additional steps to ensure highly accurate age predictors [High Rnsortium . Furtheredictors , 33, 34.Restricting DNAm array probes to the most variable sites could improve power in association studies whilst minimising array content. We show that this approach hampers variance component analyses and that phenotypes with strong epigenetic correlates are the most robust to changes in the number of available probes. Further, loci with an mQTL and with intermediate DNAm levels are central to epigenetic predictions of clinically relevant phenotypes. Our results provide methodological considerations towards the goal of selecting reduced array content from existing methylation microarrays, which can afford more cost-effective methylomic analyses in large-scale population biobanks. However, substituting or removing probes results in alterations to chip design and possibly the background physiochemical properties of the array. Therefore, it is not appropriate to assess the transferability of the present findings to other, related platforms. Nevertheless, our data demonstrate that strategies aiming to minimise arrays using fewer probes must carefully select CpG or probe content in order to maximise epigenetic predictions of human traits.Details of Generation Scotland (GS) have been described previously , 27. GS DNAm was measured using the Infinium MethylationEPIC BeadChip at the Wellcome Clinical Research Facility, Western General Hospital, Edinburgh. Methylation typing in the first set (n = 5200) and the second set (n = 4585) was performed using 31 batches each. Full details on the processing of DNAm data are available in Additional file probe = 4970 excluded, 393,654 remaining) [There were 758,332 sites in the training sample (n = 4450) following quality control. First, we restricted sites to those that are common to the 450K and EPIC arrays to allow for generalisability to other epigenetic studies . We excluded loci that were predicted to cross-hybridise and those with polymorphisms at the target site, which can alter probe binding (nmaining) , 37. Theprobe = 144,150 excluded). Hypo- and hypermethylated sites had a mean SD of 0.01 . Probes with mean Beta-values between 10 and 90%  had a mean SD of 0.03 . Second, we excluded 133,758 probes that overlapped with known blood-based mQTLs . There were 115,746 remaining sites, which represented the ‘variable non-mQTL probes’ subset. We then extracted the 50,000, 20,000 and 10,000 non-mQTL probes with the highest SDs  . In thisThe variance component estimates are analogous, but not equivalent, to SNP-based heritability estimates , 39. Howbiglasso [2 estimates that fall far below SNP-based heritability estimates [Least absolute shrinkage and selector operator (LASSO) regression was used to build DNAm-based predictors of eighteen phenotypes. The R package biglasso  was implstimates . Here, o2) was calculated by subtracting the R2 of the full model from that of the null model (shown below). For the FEV and FVC predictors, height was included as an additional covariate in both models. For the age predictors, the R2 value pertained to that of the epigenetic score without further covariates.Linear regression models were used to test for associations between DNAm-based predictors (i.e. epigenetic scores) for the eighteen traits and their corresponding phenotypic values in the test sample. The incremental r-squared . The sub-sampled sets were sampled from ‘all available probes’ . We also generated 100 sub-samples of 115,746, 50,000, 20,000 and 10,000 probes, and not 1,000 sub-samples, for LASSO regression in order to lessen the computational burden.We tested whether variance components and incremental RIlluminaHumanMethylationEPICanno.ilm10b4.hg19 package in R [probe = 115,746) and not from ‘all available probes’  as the latter contains probes with and without mQTLs, which show different genetic architectures [We tested whether highly variable probes were significantly over-represented or under-represented for genomic and epigenomic annotations. Annotations were derived from the age in R . Annotattectures .P < 5 × 10–8) that had mean Beta-values between 10 and 90%  [probe = 144,150). We tested the performances of the 115,746, 50,000, 20,000 and 10,000 most variable probes from each of these three classes.In addition to non-mQTL subsets , we tested two further classes of probes. First, we considered probes with a reported mQTL from GoDMC  . Second,P < 3.6 × 10–8 [probe = 393,654). To allow for comparison to other subsets, the 20,000 and 10,000 most variable EWAS Catalog probes  were extracted.We also repeated REML and LASSO regression using EWAS Catalog probes . EWAS Ca6 × 10–8  and thosAdditional file 1: Table S1. Demographic and summary data for Set 1 and Set 2 - Generation Scotland. Table S2. REML analyses - primary probe sets (non-mQTL probes). Table S3. REML analyses—permuted probe sets. Table S4. DNAm-based prediction analyses—primary probe sets (non-mQTL probes). Table S5. R2 estimates from linear DNAm-based predictors in the literature. Table S6. DNAm-based prediction analyses—permuted sets. Table S7. REML for chronological age. Table S8. DNAm-based prediction of chronological age. Table S9. Enrichment of genomic features in highly variable sites. Table S10. Description of probe sets compared in variance component and prediction analyses. Table S11. REML—mQTL probes vs. non-mQTL probes. Table S12. REML—EWAS Catalog probes vs. non-mQTL probes. Table S13. REML—hypo- and hypermethylated probes vs. non-mQTL probes. Table S14. REML for chronological age—sensitivity analyses. Table S15. DNAm-based prediction—mQTL probes vs. non-mQTL probes. Table S16. DNAm-based prediction—EWAS Catalog probes vs. non-mQTL probes. Table S17. DNAm-based prediction—hypo- and hypermethylated probes vs. non-mQTL probes. Table S18. DNAm-based prediction of chronological age—sensitivity analyses.Additional file 2: Figure S1. Phenotypic variance captured by five nested sets of probes with decreasing numbers of probes and increasing mean variabilities. Restricted maximum likelihood analyses were performed using blood DNAm and phenotypic data from 4450 volunteers in the training sample of Generation Scotland. Seventeen biochemical and complex traits are shown. The seventeen traits are arranged into six groups (A–F). Vertical bars indicate 95% confidence intervals. Alc, self-reported alcohol consumption; bmi, body mass index; cholest, total cholesterol; dBP, diastolic blood pressure; DNAm, DNA methylation; fat, body fat percentage; FEV, forced expiratory volume in one second; FVC, forced vital capacity; HDL, high-density lipoprotein cholesterol; HR, heart rate; mQTL, methylation quantitative trait locus; PckYrs, smoking pack years; sBP, systolic blood pressure; whr, waist-to-hip ratio. Figure S2. Incremental R2 estimates for DNAm-based predictors of seventeen traits using five nested sets of probes with decreasing numbers of probes and increasing mean variabilities. LASSO regression was used to build DNAm-based predictors of seventeen traits using data from 4450 volunteers in the training sample within Generation Scotland. An unrelated sample of 2578 individuals in Generation Scotland served as the test set. The seventeen traits are arranged into six groups of three traits (A–F). Alc, self-reported alcohol consumption; bmi, body mass index; cholest, total cholesterol; dBP, diastolic blood pressure; DNAm, DNA methylation; fat, body fat percentage; FEV, forced expiratory volume in one second; FVC, forced vital capacity; HDL, high-density lipoprotein cholesterol; HR, heart rate; LASSO, least absolute shrinkage and selection operator; mQTL, methylation quantitative trait locus; PckYrs, smoking pack years; sBP, systolic blood pressure; whr, waist-to-hip ratio. Figure S3. Correlation structure between raw (i.e. unadjusted) phenotypes in the training and test samples within Generation Scotland. The training (A) and test samples (B) had 4450 and 2578 unrelated individuals, respectively. Alc, self-reported alcohol consumption; bmi, body mass index; cholest, total cholesterol; dBP, diastolic blood pressure; fat, body fat percentage; FEV, forced expiratory volume in one second; FVC, forced vital capacity; HDL, high-density lipoprotein cholesterol; HR, heart rate; PckYrs, smoking pack years; sBP, systolic blood pressure; whr, waist-to-hip ratio. Figure S4. Correlation structure between residualised phenotypes in the training and test samples within Generation Scotland. The training (A) and the test samples (B) had 4450 and 2578 unrelated individuals, respectively. Phenotypes were adjusted for chronological age and sex (and height for FEV and FVC). Age was not adjusted but is included for completeness of comparisons. Alc, self-reported alcohol consumption; bmi, body mass index; cholest, total cholesterol; dBP, diastolic blood pressure; fat, body fat percentage; FEV, forced expiratory volume in one second; FVC, forced vital capacity; HDL, high-density lipoprotein cholesterol; HR, heart rate; PckYrs, smoking pack years; sBP, systolic blood pressure; whr, waist-to-hip ratio.Additional file 3. Supplementary methods.


By improving the diameter of nanoparticles This study aimed to analyze the momentum and thermal transport of a rotating dusty Maxwell nanofluid flow on a magnetohydrodynamic Darcy–Forchheimer porous medium with conducting dust particles. Nanouids are the most important source of effective heat source, having many applications in scientific and technological processes. The dust nanoparticles with superior thermal characteristics offer a wide range of uses in chemical and mechanical engineering eras and modern technology. In addition, nanofluid Cu-water is used as the heat-carrying fluid. The governing equations for the two phases model are partial differential equations later transmuted into ordinary ones via similarity transforms. An efficient code for the Runge–Kutta technique with a shooting tool is constructed in MATLAB script to obtain numeric results. The study is compared to previously published work and determined to be perfect. It is observed that the rising strength of the rotating and magnetic parameters cause to recede the  Non-Newtonian fluids do not follow Newton’s viscosity law, having continuous viscosity regardless of stress. Non-Newtonian nanofluids are employed in various technological, vehicle, and typical housekeeping machinery applications. When external forces influence non-Newtonian fluids, their density might change, resulting in more fluids or solids. To develop innovative non-Newtonian nanofluid features, mathematicians worldwide are exploring novel research models throughout all periods of every day and night. Firstly, Powell and Trying introduced non-Newtonian fluid in 1944. Wang et al.  deliberaNanoparticles are tiny particles with a diameter range of (1–100) nm. For the human eye, these are invisible, having chemical and physical properties that differ significantly from their bigger material counterparts. Experts in thermal engineering have embraced the effectiveness of nanofluids due to improvements in heat transmission throughout fluid movement. The nature of the base fluid and nanoparticles underpins the previously described progress. Physical features include nanoparticle concentration and temperature impacts on the mass-to-density ratio and viscosity. Thermal conductivity, specific heat capacity at various concentrations of nanoparticles, and nanoparticles’ size and temperature are some of the thermal characteristics. According to Jama et al. , the nanResearchers in thermal engineering have embraced the effectiveness of nanofluids due to improvements in heat transmission throughout fluid movement. The nature of the base fluid and nanoparticles underpins the previously described progress. Physical features include nanoparticle concentration and temperature impacts on the mass-to-density ratio and viscosity, thermal conductivity, specific heat capacity at various concentrations of nanoparticles, and the size and temperature of nanoparticles. Nanofluids have multiple features, including nanoparticle concentration, pressure drop, friction factor, and nanoparticle radius.Because of its unique thermo-physical features and heat transmission efficiency, various researchers and analysts have been driven to examine the magnetohydrodynamic flow of viscous particles across a stretched surface due to its beneficial applications in daily life. Magnetohydrodynamic characteristics of metallic materials, such as plasma, metals, seawater, liquid, and electrolytes, are determined. Magnetic medicines, astrophysical detection, and engineering applications may benefit from MHD. Zhang et al.  investigDue to its wide-ranging uses in sophisticated technologies, engineering, and other branches of science, the flow across a spinning disc has become an advanced subject of research for investigators and engineers. Centrifugal pumps, aeronautical science, engineering areas such as rotating machinery, gas turbine rotors, thermal power-producing systems, air vacuuming devices, and medical equipment are only a few real-world applications. For the very first time, Von Karman studied flow by a rotating disk. Khan et al.  discusseTwo-phase fluid flow has become more important in the industrial and engineering sectors. One phase is assumed to be dust, while the other is fluid. Large-scale industrial and engineering applications have used dusty nanofluids made of dust substances. As a result, multiple researchers have studied the dusty fluid flow over various surfaces while suspending nanoparticles. Mahanthesh et al.  investigBy analyzing the above literature survey, we deduce that no research has been carried out on the MHD rotating flow of Maxwell fluid with dust particles and nanoparticle radius. Therefore, the prime purpose of the present investigation is to study the outcomes of nanoparticle diameter on the dynamic of Maxwell dusty fluid. Thus, this study explores the consequence of magnetic and Coriolis force effects on dusty fluid flow across extending sheets. The related nonlinear partial differential equations are converted to a system of coupled nonlinear ordinary differential equations by utilizing appropriate similarity modifications. The numerical outcome of the local skin friction coefficient and the local Nusselt number are examined in graphical and tabular form for different physical parameters. The inclusion of dust and nanoparticles with base fluid formed the dusty nanofluid. This numerical report has enormous applications in chemical and mechanical engineering, and others areas, such as the cooling of nuclear reactors, heat exchangers, retrieval of crude oil, waste water treatment, and power technology.z-axis. Fluid rotates about the z-axis with constant velocity A three-dimensional laminar rotating flow of incompressible dusty Maxwell, the dynamics of water conveying copper nanoparticles over a stretching sheet, is assumed. The sheet is stretched in the For dusty particle flowThe dimensional boundary conditions areBy introducing similarity transform (10)u^=aEquation  is identFor dusty phaseThe important physical quantities of interest are discussed in this section. The Nusselt coefficients The local Reynolds number A system of ordinary differential equations represents the flow model. Algorithms (11)–(16) are numerically solved using the effectiveness and strength of numerical computing in the Runge-Kutta approach using MATLAB. Refs. ,53 proviThe corresponding boundary conditions are as follows:This research aims to investigate Maxwell nanofluid flow and melting thermal transport across a stretched surface with fluid–particle suspension. The Runge–Kutta approach solves the governed highly nonlinear expressions with associated conditions coded in MATLAB. The numerical code validation is obtained by comparing the numerical outcomes of the current and past results of skin friction ,48 and Nx-direction for the fluid phase, and x-direction for the dust phase, respectively. It can be seen that the velocity lowered while the temperature boundary layer rose with improving the M inputs. The influence of a magnetic field on an electrically conducting fluid causes the resistive force that tends to reduce the momentum while increasing the thermal boundary layer. Because of this, the magnetic field impact has a wide range of control-based applications, including magnetohydrodynamic ion propulsion, electromagnetic metal casting, magnetohydrodynamic power production, etc. The velocity and thermal boundary layer for different inputs of the diameter of nanoparticles are portrayed in x-axis while y-axis for the fluid phase, and x-axis while y-axis for the dust particles, respectively. It is observed that the momentum profile decreased. The reduced viscosity of water-based nanofluid caused due to a larger copper nanoparticles radius leads to a rise in momentum at all levels of energy flow due to the concentration gradient. When the energy flow owing to the concentration gradient is sufficient in magnitude, a drop occurs in temperature distribution over the domain. The other side shows the impact of the rotation parameter x-direction declines and in the y-direction, it enhances fluid and dust particles with improving M and M, From diagrams a,b that Throughout all levels of energy flux caused to the concentration gradient, a decrease in the viscosity of water-based nanofluid due to a larger radius of copper nanoparticles produces an increase in the momentum boundary layer.x and y axis velocities in the two phase fluid to recede, but the temperature function exhibits an opposite trend.It is observed that the growing strength of the rotating and magnetic parameters cause the x- and y-axis velocities in the two-phase fluid, but the temperature is upgraded.The higher input in the porosity and Forchheimer number causes a decline in the magnitude of the A significant decline in the thermal boundary layer over the domain, caused due to enhancing the radius of Copper nanoparticles, is feasible when the heat flux caused to the concentration gradient is sufficiently higher in magnitude.The inclusion of dust particles or nanoparticles both cause to decline the primary and secondary velocities of fluid, and also dust particles decrease the temperature, but nanoparticles enhance the base fluid temperature.The Nusselt number is decreased negatively against higher values for higher values of The present results are compared with the past literature to validate the results.An effort has been made to investigate the importance of raising the radius of nanoparticles and heat flux due to the thermal boundary gradient in the dynamic of the Maxwell nanofluid across the stretched sheet. The non-dimensional boundary value problem is resolved numerically. The code of the Runge–Kutta method is developed in a MATLAB script. Based on the results, it is reasonable to conclude the following:


Osteosarcoma (OS) is the most common primary bone malignancy. Chemotherapy plays an essential role in OS treatment, potentially doubling 5-year event-free survival if tumour necrosis can be stimulated. The canonical Wnt inhibitor Dickkopf-1 (Dkk-1) enhances OS survival in part through upregulation of aldehyde-dehydrogenase-1A1 which neutralises reactive oxygen species originating from nutritional stress and chemotherapeutic challenge.A vivo morpholino (DkkMo) was employed to block the expression of Dkk-1 in OS cells. Cell mitosis, gene expression and bone destruction were measured in vitro and in vivo in the presence and absence of doxorubicin (DRB).DkkMo reduced the expression of Dkk-1 and Aldh1a1, reduced expansion of OS tumours, preserved bone volume and architecture and stimulated tumour necrosis. This was observed in the presence or absence of DRB.These results indicate that administration of DkkMo with or without chemotherapeutics can substantially improve OS outcome with respect to tumour expansion and osteolytic corruption of bone in experimental OS model. Osteosarcoma (OS) is the most common primary bone malignancy. Particularly common in paediatric patients, it accounts for ~9% of paediatric cancer deaths . The curHigh expression of Dickkopf–Wnt-signalling pathway inhibitor-1 (Dkk-1) occurs in various cancers, including multiple myeloma and OS –14. Our Herein, we demonstrate that through inhibiting Dkk-1 transcription by means of a vivo morpholino (DkkMo), it is possible to (i) reduce the expansion of MOSJ-Dkk-1 tumours in vitro and vivo, (ii) preserve bone volume and architecture in vivo and (iii) stimulate necrosis of the tumour. DkkMo had the capacity to perform these functions in the presence or absence of DRB, and the level of tumour growth inhibition by DkkMo when administered as a single agent was equivalent to that observed by DRB at a high dose. In contrast with DRB, DkkMo did not cause weight loss in mice. RNA sequencing indicated that DkkMo stimulated cell death and necrotic mechanisms in tumours. Collectively, these results indicate that administration of DkkMo in the presence or absence of chemotherapeutics has the capacity to substantially improve outcomes with respect to OS tumour expansion and osteolytic corruption of bone.MOSJ-pLenti cells were generated and cultured as previously described .Vertebrate animals were utilised in accordance with a protocol approved by the Texas A&M Institutional Animal Care and Use Committee. MOSJ-Dkk-1 cells were employed in the OS xenograft model as described .3D Slicer software was used for bone volume and deformation measurements. Three-dimensional renderings of the tumour-bearing and contralateral fibulae were constructed using the Otsu thresholding method. Model to model distances was calculated with the signed closest point approach.Samples are sequenced by BGI Genomics .A U-net architecture based Convolutional Neural Network (CNN) was built using TensorFlow v.2.3.0 , 18.GraphPad Prism version 8.00 and R for Mac was used for statistical analysis.Previously we demonstrated that expression of human Dkk-1 by the chondroblastic OS sarcoma cell line MOSJ  . These results indicate that the result of blocking Dkk-1 by DkkMo results in the predictable outcome of upregulation of cWnt signalling. DE genes upregulated under confluent conditions did not exhibit the same degree of similarity to Wnt upregulated genes, presumably due to noise attributed to massive upregulation of apoptotic and stress pathways that occurred with the starvation of the confluent monolayers.To gain broader insight into the effects of Dkk-1 blockade, MOSJ-Dkk-1 cells at logarithmic and confluent phases of growth were exposed to DkkMo for 6 days and mRNA was recovered for high-throughput RNA sequencing (HTS). The rationale for these culture conditions was to induce stress through mitosis or starvation, respectively. Differentially expressed (DE) sequences (by > 1.5-fold) between DkkMo-treated and scrMo-treated cultures were ) and DkkMo (2.5 and 5.0 μM) for 2, 4, 6 and 9 days. DRB alone killed approximately half of the cells, but in the presence of 5.0 μM DkkMo, this rate increased to approximately 100% at all time points , DkkMo (12.5 mg kg−1), or combination, by intraperitoneal injection every 48 h. The DRB dose was the highest that could be administered without significantly affecting survival  MOSJ-Dkk-1 OS were initiated in hindlimbs of immune-deficient nude mice followed by administration of DRB  and breadth of the distribution (Padj = 0.05 with DkkMo distributions significantly narrower), indicating that DkkMo reduced bone deformation. The same trend was evident for the combination group, but p-values were larger , suggesting that DRB might hinder the osteoprotective action of DkkMo. MicroCT measurement of the tibias and fibulas further confirmed that DkkMo reduced a tumour-associated drop in bone mineral density  and comparison between DRB-treated and combination specimens (n = 4) were performed. After processing, lists of DE transcripts were generated  and DkkMo specimens (To examine whether DkkMo could perturb genes involved with cell motility and migration, differentially expressed genes in the NT versus DkkMo group and in the DRB versus combination groups were plotted against selected GO terms related to cell migration, attachment and metastasis. DkkMo perturbed gene expression over several of these GO terms, with particular emphasis on actin reorganisation (Supplementary Fig. Aldh1a1 is a marker for a cancer stem cell or tumour initiator phenotype , 39 and −1 [The first association between Dkk-1 and osteolytic malignant bone disease was demonstrated in multiple myeloma  then for−1 , 12. To −1 , vivo mo−1 . Indeed,−1  and shor−1  and here−1 . The rol−1 , 31, 47,−1  reported−1 . Akt has−1 . This hyFrom the in vitro HTS studies, two hub and DE genes were identified with the potential to contribute to tumour progression. Hsd17b4, encoding 17β-hydroxysteroid dehydrogenase/D-3-hydroxy acyl-coA dehydrogenase, is downregulated when treated with DkkMo. Hsd17b4 is responsible for deactivation of oestrogens and androgens as well as oxidation of fatty acids and other complex substrates . It is c50 values between MOSJ-Dkk-1 cells and MOSJ-pLenti cells was also greatest when the rates of proliferation were highest. These results suggested that DRB is at its most toxic during proliferation and that Dkk-1 provides most protection at this time, concurring with reports that chromatin is susceptible to DRB-induced damage during mitosis [Since DkkMo has the capacity to sensitise MOSJ-Dkk-1 cells to stress, DkkMo may synergise with chemotherapeutics that induce oxidative damage. DRB is frequently employed as an adjuvant treatment for OS, and is known to cause oxidative damage through induction of ROS . In the  mitosis  and Dkk- mitosis , 54.Given the role of cWnt in the stimulation of proliferation in many systems and cancers , a pro-tOrthotopically implanted MOSJ-Dkk-1 cells rapidly generate tumours in nude mice. When DRB was administered at a high dose, tumour expansion was not significantly affected, whereas DkkMo administered as a single agent significantly blunted tumour growth. Given that synergy was observed between DRB and DkkMo in vitro, it is surprising that it was not observed in vivo. One explanation is that DRB triggers expression of endogenous Dkk-1 from MOSJ cells and this in turn competes with DkkMo. While DRB did upregulate endogenous Dkk-1 transcription by about 30%, the expression of murine Dkk-1 is negligible compared to the human form expressed by MOSJ-Dkk-1 cells . The in MOSJ-Dkk-1 cells form highly osteolytic tumours in nude mice accompanied by significant bone deformation. We employed μCT scans to perform quantitative topological comparisons between the contralateral and tumour-bearing tibiae and found that untreated tumours exhibited substantial bone deformation that was exacerbated by DRB. When DkkMo was administered, the bone exhibited a healthier topological profile, even in the presence of DRB, indicating that DkkMo may act to protect bone from detrimental effects of treatments as well as the tumour. While immunological blockade of Dkk-1 preserves bone in experimental models  and multTumours were subjected to HTS to gain mechanistic insights into the effect of DkkMo. The dsRed label was utilised to exclude as much extraneous tissue as possible, but the HTS data originating from in vivo specimens contrasted significantly with data from cultured cells. Differences in gene expression between the in vivo and in vitro HTS datasets are likely to be attributable to the contribution of host tissue such as blood, tumour-associated stroma and immune cell contributions secondary to responses to the host. Nevertheless as observed in vitro, DkkMo upregulated cell death-related gene ontologies, with Aldh1a1 and Hsd17b4 both identified as downregulated transcripts and as hub genes. The in vitro and in vivo detection of Aldh1a1 and Hsd17b4 as downregulated sequences with DkkMo treatment, and their potentially interrelated roles in the metabolism of organic substrates, suggests a novel role for Hsd17b4 as a mediator of drug resistance. Unexpectedly, there was representation from immune and hematopoietic GO terms with DkkMo treatment, implying that Dkk-1 might also regulate immune processes. These data should be regarded with some caution given the T-cell-depleted status of nude mice, but Dkk-1 has been reported to stimulate infiltration of macrophages and neutrophils , 62 and MOSJ-Dkk-1 cells are an excellent model to study primary OS expansion and bone destruction, but little is known about their ability to metastasize. Attempts to detect MOSJ-Dkk-1 cells in the lungs of mice by PCR were unsuccessful, but DkkMo perturbed expression of several genes involved in cell attachment, migration and metastasis of tumours. This included downregulation of Arpc2, a regulator of actin polymerisation and mediator of tumour migration in several tissues , 38, 64.Small interfering RNA has been employed to block Dkk-1 and exhibit neuroprotection in a model of intracerebral haemorrhage , inhibitSupplemental tablesFigure S1Figure S2Figure S3Figure S4Figure S5Figure S6Figure S7Figure S8Supplemental materials


Parachauliodes fishflies and Protohermes dobsonflies expel relatively larger amounts of fluid than the other genera of Corydalidae observed herein. The vigorous expelling of fluid when they are disturbed may have the function of predation avoidance. In the future, we need to examine how this behavior is effective at deterring predators and why it develops more in some groups of Megaloptera.In most holometabolous insects, meconial fluid produced via the Malpighian tubules at the pupal stage is all discharged from the anus at adult emergence. However, adult alderflies , fishflies , and dobsonflies  of the Megaloptera order are likely to expel the meconial fluid when they are disturbed after emergence. In this study, we compare this fluid expelling behavior among 1 species of the alderfly, 12 species of four fishfly genera, and 15 species of three dobsonfly genera. The response of expelling to artificial stimulation occurs less frequently and the amount of expelled fluid is relatively smaller in alderflies than in the other groups. Chemical secretions are an effective means by which insects may deter potential enemies, already being studied extensively with regard to their chemicals, synthesis, toxicity, and functions. However, these defensive secretions have been little studied in Megaloptera. Herein, the fluid expelling behavior of adult alderflies , fishflies , and dobsonflies , all of the order Megaloptera, is described in detail regarding the timing and possible function of this behavior. When disturbed artificially, both males and females could expel fluid from the anus. However, the frequency of expelling was much lower in alderflies than in fishflies and dobsonflies. The amount of expelled fluid relative to body weight was also smaller in alderflies. In fishflies and dobsonflies, the amount of expelled fluid decreased with adult age, probably because the fluid is little replenished once expelled. The cream-colored fluid seems to be meconial fluid produced via the Malpighian tubules at the pupal stage, which is usually discharged at adult emergence in most other holometabolous insects. However, adult fishflies and dobsonflies often expel it vigorously by bending their abdomen when disturbed after emergence. Thus, the fluid expelling may be an anti-predatory behavior, particularly in younger adults that can expel a relatively large amount of fluid. Euxoa auxiliaris (Grote) can expel it in response to a disturbance after emergence, although the expelled fluid peaks in quantity just after the emergence and decreases with age [Arctia plantaginis (Linnaeus) also expels the majority of their anal fluid in the first-day attack and lowers quantities during the second- and third-day attacks [Insects are physically and/or chemically protected against predation ,4,5,6,7.with age . The adu attacks .Neohermes californicus  is described to void white meconium by just-emerged adults and field-captured adults when handled [In Megaloptera, meconial fluid is also expelled from the anus at emergence , and Neo handled . We foun handled ,15,18. MThe behavior of expelling fluid was observed by stimulating adults with human hands for 1 species of alderfly, 12 species of four fishfly genera, and 15 species of four dobsonfly genera . Adults In general, adults emerged after about the 10-day prepupal and 10-day pupal period. Newly emerged adults were reared individually in glass vessels (70 mm in diameter and 90 mm in height) at 25 ± 1 °C (14 h light and 10 h dark light cycle). The glass vessels contained wet filter paper on the bottom to prevent desiccation and nylon mesh covering the top. Adults were given a sufficient amount of a 10% sucrose solution every day.Fluid expelled from the anus was sampled using a known-weight plastic bag, in which the adult thoracic part was grasped directly, with hands at the mouth of the bag. To avoid different responses depending on the handling pressure, only the first author grasped the adult thoracic parts throughout this study. Immediately after expelling, the adult was removed, and the bag was weighed again. Then, the amount of expelled fluid was calculated as the weight difference of the bag before and after expelling. This experiment was performed before giving the sucrose solution on the day, and once used, individuals were not examined again. The adult head width (between the outer margins of the right and left eyes) was measured using a digital caliper. To remove the effect of body size, the residuals of the expelled fluid weight were plotted on their phylogenetic tree .Parachauliodes continentalis , P. asahinai , Neochauliodes amamioshimanus , and N. occidentalis , and seven species of dobsonflies, namely Protohermes davidi , P. grandis , P. immaculatus , P. guangxiensis , P. similis , P. horni , and Neoneuromus maclachlani . Fluid expelled from the anus was sampled using a known-weight plastic bag, in which the adult thoracic part was grasped with hands and weighed. This experiment was performed once for laboratory bred individual adults of different ages.The effect of adult age after eclosion on the amount of expelled fluid was examined from 2017 to 2021 for four species of fishflies, namely Parachauliodes continentalis , P. japonicus , and Neochauliodes amamioshimanus , and seven species of dobsonflies, namely Protohermes davidi , P. grandis , P. immaculatus , P. similis , P. weelei , Neoneuromus indistinctus , and N. maclachlani . The adult thoracic region was grasped by hands, and the amount of expelled fluid was weighed on the day of emergence (day 0), on the 5th day, and on the 10th day for the same individuals.The recovery rate of fluid to expel was examined from 2018 to 2021 for three species of fishflies, namely U-test was used between the two groups of taxa. Spearman’s rank correlation coefficient tests were used to examine the relationship between the adult age (days after emergence) and the amount of expelled fluid in each species. The relationship between adults’ age and adults’ body size (head width) was also tested using Spearman’s rank correlation coefficient in each species, to check whether the effect of adults’ body size on the amount of expelled fluid should be excluded or not. The amount of expelled fluid was compared among the groups of adults aged 0, 5, and 10 days using the Friedman test, followed by the Dunn–Bonferroni post-hoc test to determine which group combinations had a significant difference. These tests were performed using IBM SPSS Statistics ver. 2.5.In a log–log scatterplot between individual body size (head width) and fresh weight of expelled fluid, Pearson’s correlation analysis was used, and then, the residual of the log-scale fluid weight was calculated from the regression equation for comparison among the taxa. The Kruskal–Wallis test and Steel–Dwass multiple comparison test were used for the comparisons of the residuals among the three groups of taxa, and the Mann–Whitney http://www.momo-p.com/showdetail-e.php?movieid=momo211129mn01b&embed=on). The frequency of expulsion was much lower in alderflies than in fishflies and dobsonflies (We observed that most adults expelled fluid from the anus when their body was grasped by hands (sonflies . They exy mg) expelled by individuals positively correlated with their head width (x mm) in the log–log relationship  in all combined data . This amount differed among the three dobsonfly genera Protohermes (nine species), Neoneuromus (four species), and Acanthacorydalis (two species) , and Protohermes expelled relatively more fluid than Neoneuromus, although it was statistically marginal .The body size (head width) varied among the species and genera . The aldNeochauliodes occindentalis , in which the decline in expelled fluid with age may be a side effect of their body size.The amount of fluid expelled when stimulated by hands decreased as the age of the adult advanced, although in some species, particularly those with a small sample size, this tendency was not statistically significant . In thisRepeated stimulation by hands to the same individuals at 0, 5, and 10 days after emergence resulted in a great decrease in the expelled fluid after the first stimulation, although this tendency was not statistically significant in some species . The expNeohermes californicus [Doru that have a pair of defensive glands on the fourth abdominal tergite from which they discharge fluid when disturbed [Eurycotis, Deropeltis, and Diploptera that have glands that open as slits on their abdominal segments and spray fluid [The expelling of meconial fluid from the anus by adults has been reported in several holometabolous insects ,13,14,15fornicus . In thisisturbed  and cockay fluid .E. auxiliaris and A. plantaginis when disturbed [The fluid expelled by alderflies, fishflies, and dobsonflies is similar in color to the meconium expelled by the moths isturbed ,13. In aE. auxiliaris [A. plantaginis, the majority of anal secretion was released in the first-day attack, with much lower quantities during the second- and third-day attacks [In fishflies and dobsonflies, the expelled fluid peaked in quantity on the emergence day and decreased with age . Once thxiliaris . After e attacks . Thus, tFishflies and dobsonflies are relatively large insects , Table 1Vigorous fluid expulsion itself may have the effect of discouraging predators. The use of stored metabolic wastes  may be a costless method. Adult fishflies and dobsonflies have relatively large and hard mandibles among insects , which aAdults of the Megaloptera family are capable of expelling fluid vigorously from the anus when disturbed with hands as a simulation of attack by potential predators. This fluid is cream-colored and is considered to be meconium produced via the Malpighian tubules at the pupal stage. This fluid is usually discharged at adult emergence in most holometabolous insects, but the megalopteran adults seem to use it as a defensive fluid. Adult alderflies and some fishfly and dobsonfly genera expel a small amount of fluid relative to their body size. The amount of the expelled fluid also decreases with adult age after emergence, probably because the fluid is little replenished once expelled. The effectiveness of this megalopteran fluid expelling behavior has still not been examined, but this behavior may function as an anti-predatory mechanism. More information on the chemicals present in the expelled fluid and direct evidence of the effects against potential predators are needed.