Misinformation on social media poses a serious threat to democracy, sociopolitical stability, and mental health. Thus, it is crucial to investigate the nature of cognitive mechanisms and personality traits that contribute to the assessment of news items' veracity, failures in the discernment of their truthfulness, and behavioral engagement with the news, especially if one wants to devise any intervention to stop the spread of misinformation in social media. The current research aimed to develop and test a 4-fold taxonomy classifying people into four distinct phenotypes of susceptibility to (mis)information. In doing so, it aimed to establish differences in cognitive and psychological profiles between these phenotypes. The investigated cognitive processes included sensitivity to feedback, belief updating, and cognitive judgment bias. Psychological traits of interest included the Big Five model, grandiose narcissism, anxiety, and dispositional optimism. The participants completed online surveys that consisted of a new scale designed to classify people into one of four phenotypes of susceptibility to (mis)information, advanced cognitive tests, and reliable psychological instruments. The four identified phenotypes, Doubters, Knowers, Duffers, and Consumers, showed that believing in misinformation does not imply denying the truth. In contrast, the numerically largest phenotypes encompassed individuals who were either susceptible (Consumers) or resistant (Doubters), in terms of veracity judgment and behavioral engagement, to any news, regardless of its truthfulness. Significantly less frequent were the phenotypes characterized by excellent and poor discernment of the news' truthfulness . The phenotypes significantly differed in sensitivity to positive and negative feedback, cognitive judgment bias, extraversion, conscientiousness, agreeableness, emotional stability, grandiose narcissism, anxiety, and dispositional optimism. The obtained results constitute a basis for a new and holistic approach in understanding susceptibility to (mis)information as a psycho-cognitive phenotype. The problem of misinformation has received increasing research interest as events such as the 2016 United States presidential elections or the Brexit referendum in Great Britain drew attention to the power of fake news in influencing public opinion about highly consequential issues , our knoDeciding whether to believe information involves several cognitive and motivational processes, including the ability to differentiate between truth and falsehood based on analytical and reflective reasoning, the ability to update beliefs in response to new information, sensitivity to positive and negative reinforcement, and optimistically/pessimistically biased judgment. The role of analytical and reflective reasoning in veracity judgment has been recently demonstrated experimentally by several studies. They show that people are able to override incorrect intuitions via analytical thinking –6 and thIndividual schemas of cognitive processes, along with emotional and behavioral patterns, constitute a more general concept of personality . VariousFigure 3). Such a framework offers a structure for characterizing and quantifying individual differences in susceptibility to (mis)information and allows for a nuanced test of its underlying cognitive and psychological traits.The constant evaluation of information is a fundamental process of human cognition and is integral to learning, social engagement, and decision-making , 21, 22.In the present study, using Prolific Academic linked with Qualtrics and Millisecond Inquisit web testing platforms, we recruited a sample of participants and assessed their suscpetibiliy to various true and false news regarding the COVID-19 pandemic. Based on this assessment, we classified each participant into one of the four phenotypes of information susceptibility: Consumers, Knowers, Duffers, and Doubters. The participants were further tested with regard to the abovementioned cognitive processes using experimental paradigms, such as the Ambiguous-Cue Interpretation test [ACI ], assess*Power 3.1  and all participants answered all checks correctly. The targeted number of participants was 200 adult Americans; however, due to dropouts as a result of technical problems during the experiment (n = 16), we analyzed data for N = 184 . The final sample included 87 males, 87 females, and 8 non-binary people (n = 169) declared that the COVID-19 pandemic is a danger , while the remaining 13 people declared that they believed the COVID-19 pandemic is a hoax information was measured using a newly created scale based on 24 news headlines in a Facebook-like format. The topic of the news was connected to the COVID-19 pandemic. Half of the news presented true information, which was obtained from research reports and official World Health Organization guidelines and statistics . The othDo you think the news above is true?”, where 1 was “definitely false” and 6 was “definitely true”), and the probability of engagement with it , and willingness to share . There was a positive correlation between willingness to share and willingness to like .Susceptibility to information was defined on two levels. The participants were asked to evaluate each item on the scale in terms of its veracity , fake news veracity judgment (Cronbach's α = 0.68), engagement with true news rating (Cronbach's α = 0.93), and engagement with fake news rating (Cronbach's α = 0.88).Based on the scores from SIS and a median split of the averaged true and averaged fake news veracity ratings, each participant was assigned to one of the four phenotypes of (mis)information susceptibility: Consumers , Knowers (the ones highly rating the veracity of true news and low rating the veracity of fake news), Duffers (opposite to the knowers: highly rating the veracity of fake news and low rating the veracity of true news), and Doubters . The same classification was conducted based on engagement with the news scores. Thus, this resulted in the differentiation of two separate types of phenotypic classification, one based on veracity and the other on engagement ratings .To experimentally evaluate cognitive judgment bias (optimism/pessimism), the participants were tested using the ACI test. This procedure was adapted from Schick et al.  and modiTo further test whether an optimistic bias could differ in phenotypes of susceptibility to (mis)information, participants completed a Belief Updating Test (BUT) . During where O is Optimistic belief updating, P is Pessimistic Belief Updating, E1 is First estimation, and E2 is Second estimation.P = 0, no belief update occurred. For O or P = 1, the belief update relied completely on the feedback. When O or P > 0 and < 1, the update partially relied on the feedback. When O or P > 1, the update exceeded the feedback, and when O or P < 0, the update was negatively modulated by the feedback. The final two scores Pessimistic Belief Updating Index and Optimistic Belief Updating Index were calculated as the means from the respective events.For O or To test how sensitivity to feedback differs in susceptibility to (mis)information, the participants completed the Probabilistic Reversal Learning (PRL) .In this task, for each trial, two stimuli  a correct response followed by positive feedback, (2) a correct response followed by negative feedback (probabilistic punishment), (3) an incorrect response followed by negative feedback, and (4) an incorrect response followed by positive feedback (probabilistic reward). These four types of events were then subjected to the Win-shift/Lose-shift analysis, where behavior was analyzed according to the outcome of each preceding trial to assess the sensitivity of participants to positive and negative feedback. Rewarded outcomes followed by a decision to shift the response (Win-shifts) were counted separately for the correct and incorrect responses and expressed as a ratio of all rewarded outcomes on a given stimulus. These Win–shifts ratios were used as a measure of sensitivity to either true (if the rewarding outcome followed the choice of the correct stimulus) or misleading (if the rewarding outcome followed the choice of the incorrect stimulus) positive feedback . Conversely, Lose-shift ratios were calculated by dividing punishing outcomes after which the subject changed the choice by the total number of punished trials on a given stimulus. These Lose-shift ratios represented sensitivity to either true (when the punishing outcome followed the choice of the incorrect stimulus) or misleading (when the punishing outcome followed the choice of the correct stimulus) negative feedback . Additionally, by consecutively choosing 10 correct patterns, the participants achieved reversal. The number of achieved reversals indicated cognitive flexibility, as it requires the participants to be able to notice and adapt to constantly changing rules of the test .I see myself as extraverted, enthusiastic”) is defined by a mean score of answers to two questions given on a 7-point Likert scale . It also revealed a generalized effect of the veracity rating group, i.e., Participants who highly rated the true news also highly rated fake news .Notably, the intergroup difference was significantly more pronounced for true news ratings than for fake news ratings . Moreover, it also revealed a generalized effect of news rating group, i.e., Participants who highly rated fake news also highly rated true news . Again, the intergroup difference was significantly more pronounced for fake news ratings than for true news ratings . It also revealed a generalized effect of engagement with the news group, i.e., participants declaring high engagement with the true news were also declaring high engagement with the fake news . Notably, similar to the veracity ratings, the intergroup difference was significantly more pronounced for the declared engagement with true news than with the fake news  = 77.09, P < 0.001, Analogous to veracity ratings, the median split of the ratings of engagement with true news resulted in the differentiation of two groups of participants: those with scores above the median (highly engaged with true news) and those with scores below the median (unengaged with true news). Comparison of the ratings of engagement with fake news in these groups revealed that in general, the participants declared a higher rate of engagement with true news than with fake news . It also revealed a generalized effect of engagement with the news group, i.e., participants declaring high engagement with the fake news were also declaring high engagement with the true news . Again, similar to the veracity ratings, the intergroup difference was significantly more pronounced for the declared engagement with true news than with the fake news  or between Knowers and Duffers .The analysis of the Win-shift/Lose-shift data from PRL tests revealed a lack of significant interphenotypic differences in sensitivity to positive feedback between Doubters and Consumers  and between Knowers and Duffers .The analysis of choices following ambiguous cues in the ACI paradigm revealed a lack of significant interphenotypic differences in cognitive judgment bias between Doubters and Consumers  = 0.99, P = 0.33; Knowers vs. Duffers, t(71) = 0.14, P = 0.89) and in pessimistic belief updating .The analysis of optimistic and pessimistic belief updating indices revealed a lack of significant interphenotypic differences in optimistic belief updating . Furthermore, no significant interphenotypic differences were revealed in extraversion , emotional stability  = 0.07, P = 0.95), agreeableness  = 1.172, P = 0.25) or openness to experience .The analysis of the 5-factor model from TIPI revealed that Consumers were more conscientious than Doubters  = 1.123, P = 0.26].The TAS analysis revealed that Duffers were less anxious than Knowers  = 3.03, P = 0.003; P = 0.04; t(109) = 2.13, P = 0.04; t(109) = 3.38, P = 0.001; t(109) = 3.35, P = 0.001; P = 0.29), exploitativeness  or exhibitionism . The analysis revealed no significant differences between Knowers and Duffers in grandiose narcissism , authority , superiority , vanity , entitlement , self-sufficiency , exploitativeness , and exhibitionism .The analysis of GNS revealed that Consumers had higher scores than Doubters in grandiose narcissism  or BIS . Furthermore, there were no significant differences between Knowers and Duffers in BAS Drive , BAS reward responsiveness , BAS fun seeking  and BIS .The analysis of variables from the BIS/BAS scale revealed that Consumers scored significantly higher than Doubters in BAS drive  = 1.46, P = 0.15; Knowers vs. Duffers, t(71) = 0.82, P = 0.42) and in sensitivity to punishment .The analysis of SPSRQ-RC revealed a lack of significant interphenotypic differences in sensitivity to reward  = 2,654, P = 0.009; P = 0.02; P = 0.98), sensitivity to negative feedback , and cognitive flexibility .The analysis of the win-shift/lose-shift data from PRL tests revealed that, compared to Doubters, Consumers were less sensitive to positive feedback .Prescreening of the ACI data revealed the presence of 2 outliers (identified with the ROUT method), and these data were excluded from the analysis. In the ACI paradigm, Knowers more often identified the ambiguous tone as a cue predicting a reward than Duffers ; Knowers vs. Duffers (t(27) = 0.42, P = 0.68)), and in pessimistic belief updating .The analysis of optimistic and pessimistic belief updating indices revealed a lack of significant interphenotypic differences between the groups of interest in optimistic belief updating  and openness to experience .The analysis of the 5-factor model from TIPI revealed that, compared to Doubters, Consumers were more extraverted  = 3.31, P = 0.003; t(27) = 0.44, P = 0.66], conscientiousness , emotional stability , and openness to experience .In the case of agreeableness, Duffers scored higher than Knowers .The TAS analysis revealed that Doubters were more anxious than Consumers , authority , superiority , vanity , entitlement , self-sufficiency , exploitativeness , and exhibitionism .The analysis of GNS revealed that the Consumers had higher scores than Doubters in grandiose narcissism .The analysis of LOT-R revealed that Consumers were more optimistic than Doubters , BAS fun seeking , BAS Reward Responsiveness , and BIS .The analysis of variables from the BIS/BAS scale revealed that Consumers scored significantly higher than Doubters in BAS drive  = 3.25, P = 0.001; P = 0.25). No significant differences were revealed between Knowers and Duffers in sensitivity to reward  and sensitivity to punishment .The analysis of SPSRQ-RC revealed that Consumers were more sensitive to reward than Doubters The present study proposed the concept of four phenotypes of susceptibility to (mis)information that result from combining individual veracity ratings of true and fake news and similar classification based on behavioral engagement with true and fake news. Empirical implementation of this concept provides a holistic approach to the investigation of the susceptibility to (mis)information that had not previously been operationalized in the fake news research. The results of our study also revealed that phenotypes of susceptibility to (mis)information differed with respect to several cognitive processes and psychological traits. Both ways of phenotypic classification, which were established on the basis of the veracity ratings and the one established on the basis of engagement with the news ratings, revealed statistically significant interphenotypic differences in psychological traits, including conscientiousness, anxiety, narcissism, and BAS drive. The phenotypes based on engagement with the news differed from each other in extraversion, agreeableness, emotional stability, dispositional optimism, and sensitivity to reward. Moreover, they also differed in several cognitive processes, including sensitivity to positive and negative feedback measured in the PRL tests and cognitive judgment bias measured in the ACI paradigm.When analyzing factors altering susceptibility to misinformation, it is important to consider not only the extent to which misinformation is believed in relation to true content (truth discernment) but also the overall degree to which information is believed, regardless of its truthfulness. This is important because although increasing or decreasing belief in true and false headlines to an equivalent extent does not affect truth discernment, it might still determine the effects of misinformation . To addrIn the present study, the interphenotypic differences in cognitive and psychological traits were analyzed along two different axes: one encompassing a general susceptibility/unsusceptibility to information (Consumers vs. Doubters), and the other that differentiated people who were susceptible to fake news from those who were unsusceptible to this type of information (Knowers vs. Duffers). Importantly, they were analyzed not only on the level of basic veracity ratings but also in terms of behavioral engagement . Performed analyses revealed  that theAnalysis of the second axis of vulnerability to the news, e.g., susceptibility to true vs. fake news, revealed that Duffers, distinguished based on their veracity judgments, were less anxious than Knowers. Two possible explanations might be considered. From one perspective, the existence of the pandemic is threatening, and people who deny the true information about it do so because they do not perceive it as dangerous enough to believe it. Another perspective suggests that Knowers who accept the danger of the pandemic are more anxious because of the threat behind it, whereas Duffers might find fake news anxiolytic. Whether low anxiety causes believing in false information or false information lowers anxiety is difficult to determine based on the results of this study, and the discussed causation should be further explored in future experiments.The exploration of differences between Knowers and Duffers distinguished based on engagement with true and false information revealed that Duffers were more pessimistically skewed in interpreting ambiguous cues. This observation could explain why these people prefer to share and like false information, which often presents conspiracies that generally accuse some third persons or institutions to have malicious intentions. Engagement with fake news seems to escalate together with increased agreeableness. Indeed, while in the information bubble, persons who are more agreeable might be more likely to engage with fake news than others.The empirical results reported herein should be considered in light of some limitations. Since social media constitute the natural environment where individuals interact with (mis)information, we designed our scale of susceptibility to (mis)information in a way that mimicked Facebook headlines. This approach, although broadly used in similar research, could be improved by conducting more ecological, real-time studies using algorithms tracking the behavior of users on social media platforms. This would help to eliminate situations where declared willingness to share or like given information in a survey might differ from actual sharing and liking behavior in social media. The second possible limitation might concern the online data collection and lack of control over the setting in which participants provided their responses, e.g., the PRL or ACI tests have never been previously used in the studies conducted online. This, however, has been mitigated by recruiting experienced but not professional individuals (see Methods) and performing attentional checks, which warranted motivation and devotion of the study participants.Our findings indicated the presence of various phenotypes of susceptibility to (mis)information, characterized by different clusters of cognitive and psychological traits. They also indicated that the concept of vulnerability to fake news cannot be investigated in isolation from the general susceptibility to information regardless of its veracity. Outlining the four phenotypes of susceptibility to (mis)information creates foundations for further research that should focus on the real-time behavior of people using social media and on the diagnosis of vulnerability to misinformation.https://ruj.uj.edu.pl/xmlui/handle/item/289808?locale-attribute=en.All data analyzed in this study have been made publicly available via Jagiellonian University Repository , and canThe studies involving human participants were reviewed and approved by the Bioethics Committee of Jagiellonian University in Krakow, Poland (1072.6120.66.2021 from 19 May 2021). The patients/participants provided their written informed consent to participate in this study.JP, JK, and RR obtained funding. MP, KN, AG, JP, JK, and RR designed and planned the research. MP conducted the experiment and analyzed the data. MP and RR wrote the manuscript. MP, KN, JP, PG, AG, JK, and RR revised the the manuscript and contributed to the final discussion. All authors contributed to the article and approved the submitted version.The research leading to these results has received funding from the EEA Financial Mechanism 2014-2021. Project: 2019/35/J/HS6/03498.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The RR for psychiatric symptoms in the EM group was 0.49 . Sensitivity analysis accounting for the influence of death, loss to follow-up , or potential confounders  consistently showed a lower incidence of psychiatric symptoms in the EM group. EM was consistently associated with fewer psychiatric symptoms.This is a prospective multicenter cohort study aiming to investigate the association between early mobilization (EM), defined as a rehabilitation level of sitting at the edge of the bed or higher within 72 h of ICU admission, and psychiatric outcome. Consecutive patients, admitted to the ICU for more than 48 h, were enrolled. The primary outcome was the incidence of psychiatric symptoms at 3 months after hospital discharge defined as the presence of any of three symptoms: depression, anxiety, or post-traumatic stress disorder (PTSD). Risk ratio (RR) and multiple logistic regression analysis were used. As a sensitivity analysis, two methods for inverse probability of treatment weighting statistics were performed. Of the 192 discharged patients, 99 (52%) were assessed. The patients who achieved EM had a lower incidence of psychiatric symptoms compared to those who did not (25% vs. 51%,  Critical illness may result in severe psychiatric disorders, such as depression, anxiety, and post-traumatic stress disorder (PTSD) ,2, whichImmobility induced by physical restriction is associated with the development of psychiatric disorders ,9, whileTherefore, we conducted a multicenter prospective cohort study to investigate the incidence rate of psychiatric symptoms at 3 months after hospital discharge and the association between active physical rehabilitation within 72 h of ICU admission and psychiatric symptoms. We focused on 3 months since a similar trend and incidence rate of psychiatric symptoms at 3 months and 1 year after hospital discharge have been observed ,15.This multicenter prospective cohort study was approved by the Ethics Committee of Nagoya Medical Center (2018093) and eight other participating hospitals  and registered in UMIN (ID: 000036503). We followed the STROBE guidelines , and allConsecutive patients, up to 25 patients in each participating hospital, who stayed in the ICU for more than 48 h between June and December in 2019, were eligible for enrollment. Patients less than 18 years of age, unable to walk independently before admission, with neurological complications, lacking communication skills due to pre-existing mental diseases, or in a terminal state were excluded . Our stuThe early goal-directed protocol for rehabilitation ,20,21 waThe protocol provides patients with five rehabilitation levels  based on their medical condition. At each participating site, ICU physicians or physiotherapists made the final decision of the rehabilitation level based on the patient’s condition while referring to the protocol. All patients were supposed to receive at least one rehabilitation session per day for 20 min on weekdays.Regarding other ICU care, all participating hospitals followed the 2018 Clinical Practice Guidelines  and the After transfer out of the ICU, all patients underwent rehabilitation, such as muscle strengthening, balancing, walking, and stair exercises, for more than 20 min on weekdays by physical or occupational therapists according to the rehabilitation policy in the general ward of each hospital without any specific protocol.Baseline characteristics were collected at the time of ICU admission and during the ICU stay by co-investigators at each hospital, including age, gender, body mass index, Charlson comorbidity index , BarthelThe primary outcome was the incidence of psychiatric symptoms at 3 months after hospital discharge, which was defined as the presence of at least one of three symptoms: depression, anxiety, or PTSD. Depression and anxiety were assessed using the HADS that contains 14 items, seven for anxiety assessment and seven for depression, with a score of 0–3 for each item. Within a maximum score of 21 for each subset for depression or anxiety assessment, the presence of depression or anxiety was defined as a score of 8 or more . PTSD waSecondary outcomes were the scores on HADS subsets for depression or anxiety and the IES-R score for PTSD at 3 months after hospital discharge, as well as the change in each score between hospital discharge and at 3 months.Other variables included the EQ-5D-5L which is a standardized assessment for HRQoL  at 3 monA sample size of 240 patients is needed with 80% power and a two-sided significance level of 0.05, under the assumption of a follow-up achievement rate of 80%, 60% of non-EM patients will develop psychiatric symptoms and EM patients will have a 20% reduction based on previous studies ,15,34.2 test or Fisher’s exact test for nominal variables, as appropriate. Before using a non-parametric test, the distribution of each parameter was evaluated with the Shapiro–Wilk test.Data are presented as a median with interquartile range or as a number with percentage. The Mann–Whitney U test was used to analyze continuous variables and the χIn addition to the comparison of baseline characteristics between the two groups, given the influence of death and loss to follow-up, we also compared them between patients discharged from the ICU and those who completed follow-up at 3 months. The same analysis was conducted among patients discharged from ICUs and those who completed follow-up both in the EM and non-EM groups.Multiple logistic regression analysis was performed to identify an association of the primary outcome with the following covariates: Age, male gender, Barthel index before hospitalization, ICU admission diagnosis, APACHE II score, and use of mechanical ventilation, continuous analgesia, continuous sedation, steroids, and neuromuscular blocking agents, and dialysis, which were considered as factors related to the primary outcome in previous reports  35,36,3,45,46,47As a post hoc analysis, two methods for inverse probability of treatment weighting (IPTW) statistics were used. The first adjusted the influence of death and follow-up loss and the second adjusted as many confounding factors from baseline characteristics such as severity, baseline comorbidity, ICU admission diagnosis, consciousness level, which could affect the outcomes. The methodological details of the analysis are shown in p-values of <0.05.All analyses were performed using SPSS version 23.0 . Statistical tests were two-sided, and statistical significance was defined as p = 0.026) and the use of mechanical ventilation , steroids, neuromuscular blockade , and dialysis  (p < 0.001), and a higher IMS score during their ICU stay .Of the 1014 patients screened, 203 were enrolled . Of thes= 0.037) . The aveConsidering the bias due to death and loss to follow-up, no differences were observed in all baseline characteristics comparing patients discharged from the ICU and who completed the 3-month follow-up in each comparison .p = 0.032] even after adjustment for the baseline characteristics of the two groups, whereas the incidence at the time of hospital discharge was not different  (The incidence of psychiatric symptoms at the 3-month follow-up point was significantly lower in the EM group than in the non-EM group [odds ratio (OR): 0.27, adjusted = 0.965) .p = 0.026) and had a significantly lower HADS subset score for anxiety (adjusted p = 0.004) and IES-R (adjusted p = 0.009) compared with the non-EM group. The EM group demonstrated no significant differences in the incidence of depression, anxiety, and PTSD, the HADS subset scores for depression and anxiety, and the IES-R score at the time of hospital discharge. When comparing the assessment scores at hospital discharge and at 3 months follow-up, only changes in the HADS subset scores for anxiety in the EM group were significantly higher (adjusted p = 0.032).At 3 months follow-up, the EM group showed a significantly lower incidence of PTSD , anxiety , and PTSD  at 3 months follow-up were lower in the EM group, while no significant difference in the risk ratios was observed at the time of hospital discharge : 0.29–0.83, ischarge .p = 0.011) and anxiety , and the IES-R score  at 3 months follow-up  at 3 months follow-up. Changes in the HADS subset scores for anxiety were significantly associated with achieving EM   .There was no difference between the two groups in the EQ-5D-5L at 3 months follow-up and at hospital discharge. The EM group had a shorter length of ICU and hospital stays, a higher Barthel Index at hospital discharge, and a lower incidence of delirium and ICU-AW. .p = 0.008), lower incidence of anxiety and PTSD, and a lower HADS subset score for anxiety and IES-R score at 3 months follow-up (p = 0.046) and a lower incidence of PTSD and a lower HADS subset score for anxiety and IES-R score (The model created by IPTW considering the influence of death and follow-up loss showed that EM correlated significantly with a lower incidence of psychiatric symptoms (OR: 0.28, CI: 0.11–0.73, adjusted ollow-up . In the -R score .The incidence of psychiatric symptoms was significantly lower in the EM group and EM in the ICU was significantly associated with a lower incidence of psychiatric symptoms at 3 months follow-up after hospital discharge even after adjustment for significant differences in the baseline characteristics including severity and treatments received. The results were consistent even if the influence of patient death, loss to follow-up or potential confounding factors were included in the analysis. Furthermore, the interval from ICU admission to first mobilization correlated significantly with the score of HADS subsets for Depression and Anxiety and the score of IES-R for PTSD.Mobilization, rehabilitation at the level of sitting on the edge of the bed or higher was significantly associated with improved psychiatric symptoms. A previous systematic review using mobilization in the ICU as an intervention did not show a significant improvement in psychiatric symptoms after ICU discharge . Of noteThe present study supports the idea that the effect of EM on outcomes related to psychiatric symptoms after ICU discharge as well as other clinical outcomes might be maximized if mobilization is initiated within 72 h of ICU admission. The significant correlation between time from ICU admission to achieve EM and scores for diagnosing psychiatric symptoms support this idea. The interval until initiation of mobilization might be an essential aspect of physical rehabilitation in the ICU to maximize the effect on patient outcomes.The EM group had a lower incidence of psychiatric symptoms at 3 months follow-up, but there was no significant difference between the groups at discharge. It has been reported that there is a time lag for the onset of anxiety and depression ,55. TherSeveral studies suggest an interaction between physical and psychiatric factors and these outcomes could be linked ,59. PhysThe comparability between the two groups is a primary limitation of this study. The calculated sample size could not be achieved, there was a relatively large influence of death and loss to follow-up and the study was not randomized. These factors may limit generalizability to other ICUs. Second, there are confounding factors which could not be adjusted for, such as educational history and clinical frailty. However, multivariate analysis with important clinical factors, IPTW, and propensity score matching adjusted by potential confounders showed consistent results even though there are several differences in important baseline characteristics of the two groups, for example the use of mechanical ventilation, steroids, neuromuscular blocking agents and dialysis. Third, outcomes were limited to short-term follow-up. Fourth, whether the patient could receive rehabilitation at the level of sitting on the edge of the bed or higher depended on the rehabilitation policy used in each participating hospital. Therefore, whether EM could not be provided due to poor general condition or other factors was not identified. Fifth, the psychiatric symptoms of the primary outcome of this study are diagnosed by scoring and are not based on criteria by psychiatrists or psychiatric liaison teams. Finally, patients who died in the ICU were excluded from the analysis in this study because we targeted ICU survivors. This could result in selection bias and these findings should be interpreted with caution. To further validate these results and investigate causality, a multicenter randomized controlled trial with more patients is needed.EM in the ICU is significantly associated with lower rates of psychiatric symptoms, including depression, anxiety, and PTSD, at 3 months follow-up after hospital discharge. The interval from ICU admission to mobilization might be an important parameter to maximize the beneficial effects on patient outcomes.


Bluetongue (BT) is a disease of concern to animal breeders, so the question on their minds is whether they can predict the risk of the disease before it occurs. The main objective of this study is to enhance the accuracy of BT risk prediction by relying on machine learning (ML) approaches to help in fulfilling this inquiry. Several risk factors of BT that affect the occurrence and magnitude of animal infection with the virus have been reported globally. Additionally, risk factors, such as sex, age, species, and season, unevenly affect animal health and welfare. Therefore, the seroprevalence study data of 233 apparently healthy animals (125 sheep and 108 goats) from five different provinces in Egypt were used to analyze and compare the performance of the algorithms in predicting BT risk.Logistic regression (LR), decision tree (DT), random forest (RF), and a feedforward artificial neural network (ANN) were used to develop predictive BT risk models and compare their performance to the base model (LR). Model performance was assessed by the area under the receiver operating characteristics curve (AUC), accuracy, true positive rate (TPR), false positive rate (FPR), false negative rate (FNR), precision, and F1 score. The results indicated that RF performed better than other models, with an AUC score of 81%, ANN of 79.6%, and DT of 72.85%. In terms of performance and prediction, LR showed a much lower value (AUC = 69%). Upon further observation of the results, it was discovered that age and season were the most important predictor variables reported in classification and prediction.The findings of this study can be utilized to predict and control BT risk factors in sheep and goats, with better diagnostic discrimination in terms of accuracy, TPR, FNR, FPR, and precision of ML models over traditional and commonly used LR models. Our findings advocate that the implementation of ML algorithms, mainly RF, in farm decision making and prediction is a promising technique for analyzing cross-section studies, providing adequate predictive power and significant competence in identifying and ranking predictors representing potential risk factors for BT. Bluetongue (BT) is an infectious, noncontagious, viral disease transmitted by insects and affects wild and domestic ruminants, predominantly sheep . The disMachine learning (ML) is a recently emerging subfield of computer science. Previously, data analysis relied primarily on traditional methods, which can’t handle large amounts of data perfectly, particularly in case of outliers . FortunaThe rationales for ML and traditional methods differ. Statistical models begin with model hypotheses, set the assumptions, and focus on inference. ML is primarily concerned with forecasting; the best-performing predictive model is chosen. ML's hypothesis-free nature makes it an appealing choice for complex data analysis . High-qu. In this study, we developed prediction models to investigate and assess BT-associated risk factors as an adjunct to improving veterinary health and disease screening in five Egyptian governorates. This study aims to address small data applications of ML models for classification tasks with the objectives of (1) developing ML prediction methods, including random forests (RF), decision trees (DTs), artificial neural networks (ANNs), and logistic regression (LR), producing BT results as binary output  and class probability using various BT-related variables; (2) evaluating the models’ performance and examining the contribution of variables to BT classification; and (3) performing validation of the BT prediction models to assess their reliability.The use of ML algorithms for predicting veterinary risk factors is still uncommon. Few studies have used ML algorithms to investigate BT risk factorsTable  presentsFigures , and 4 pTo the best of our belief, our study was the first, which applied and compared ML methods to assess and predict BT risk in Egypt, and epidemiologically, this is one of the few studies in veterinary medicine that will be scrutinized. We developed prediction models based on a seroprevalence study to predict BT risk factors using DT, ANN, RF, and LR models. Because of advances in biotechnology and information technology, ML application is straightforward . SeveralThe acceptable accuracy of ML algorithms in clinical studies significantly increases their application compared with conventional methods, such as LR . Romero The RF ML method has been effectively applied in several fields, including veterinary science . Althoug. [. [The predictors used in our study were all categorical and dummy coded; Loh and Vanichsetakul  recommen.  suggeste. [.  proposedThe performance metrics of the four ML models' training and testing sets were compared. According to Jiang et al. , compariFew studies have compared the efficacy of ML classification for predicting BT risk. A previous study  applied . [Regarding the results of the variable importance analysis, we discussed only the most relevant variables. RF, ANN, DT, and LR demonstrated that the most prominent predictors were old age, summer, and autumn. The age predictor was previously reported as a significant factor in the prediction of BT in studies , which a.  discover.  reportedRF, DT, and LR showed females as an important predictor related to BT seropositivity. These findings are consistent with a previous study .The algorithms take the season into account while predicting BT risk. Purse et al.  discoverIn this study, we compared ML techniques for improving BT risk prediction using small-scale data. The study findings revealed that ML approaches outperform LR. The need for further ML application in veterinary epidemiology was underlined, which might improve herd health and help veterinary decision-making, resulting in improved clinical treatment and increased profitability by keeping the disease under control. Overall, RF performed best, with the highest accuracy, AUC, and F1 score, closely followed by the ANN. Although DT had the lowest FPR, it showed the highest FNR, indicating inferior stability and predictive performance. Finally, LR was of poor quality, with markedly more deficient accuracy, precision, and F1 score than the other ML techniques utilized in the study. We may infer that while ML algorithms can handle multidimensional data, they demonstrate a promising impact in implementing prediction and classification using small datasets (233 in our study). LR may perform better with a larger sample size in epidemiological or clinical investigations.Four ML algorithms were applied to predict BT: RF, ANN, DT, and LR. LR was used as a baseline for performance comparison as it is a classic regression algorithm that focuses on binary classification problems. The performance of the models was assessed in terms of accuracy, TPR, FPR, FNR precision, F1 score, and AUC. These metrics ranged from 0 to 1. AUC measures the discriminative power of the model between presence/absence classes. For example, an AUC value of 0.5 means that the model is not better than a random guess; values 0.5–0.7 indicate poor discrimination, 0.7–0.8 acceptable, 0.8–0.9 excellent, and 0.9–1 exceptional discriminative power .Materials and methods used the data of a seroprevalence study conducted on 233 (125 sheep and 108 goats) apparently healthy animals. Samples were collected from April 2018 to March 2019 from five governorates in Egypt.Competitive ELISA has been carried out for the detection of antibodies against BTV VP7 antigen in serum samples of sheep and goats using ID Screen Bluetongue competition kit . According to instructions of the manufacturer, a positive reaction is scored when the percent of OD sample/OD negative control (S/N %) is less than 40%. Of all examined animals, 40.3% were seropositive, whereas 59.7% were seronegative.x) trained on a labelled set of training examples, N examples denotes an animal, where xX to refer to a matrix of predictors/features with N rows and d columns. Table  were considered important and kept for supervised classification. All variables showed a significant relationship with BT. Data were randomly assigned to a training dataset (80%) for model creation and a testing dataset (20%) for prediction performance verification. We used five repeats of ten-fold cross-validation on the training data to train the model and tune the hyperparameters. The tuned hyperparameters were subsequently used to build a more accurate predictive model. Finally, based on the metrics in the testing set, the ML model that showed the best discrimination and accuracy was determined to be used in predicting BT.The DT is a statistical model that aims to make an accurate prediction that minimizes the loss function, which measures the differences between observed and expected values. The model uses a set of factors to predict the response based on several hierarchical binary divisions of the data in the form of a tree, hence the name. Successive data splits produce subsets of data, and in each subset, the expected result is obtained by averaging the results of all individuals in the subset . Common This easily understood hierarchical graphical structure of the DT makes it a reliable predictive approach to support decision-making . MoreoveIn this study, DT was constructed with the rpart function in the “rpart” package in R, using GI as the criterion for selecting attributes and achieving the best overall split. The minimum size for the split was set to 25, the minimum number of observations in the terminal node was 7, and the maximum depth was 30. Based on the cross-validation  results of the base model, DT was pruned by specifying a complexity parameter (CP). Figure RF algorithm is a predictive statistical model consisting of a set of DT obtained through random and independent selection of data subsets. Predicting a class in RF is simply based on getting a majority vote for all trees. The RF is simple as it requires neither the data distribution assumptions nor the relationship between the predictors and the target variable. RF is also resistant to autocorrelation and multicollinearity issues . RF has It has a notable advantage of revealing the variable importance, an indicator that shows the impact of each variable used in the prediction . Gini anANN is an ML model that mimics the machinery of how the human brain works. It can handle complex biological data. Unlike traditional statistical methods, it is characterized by the ability to quickly identify data patterns (linear and nonlinear) and contingency effects .Multilayer perception (MLP) is a simple form of ANN that in most conditions, comprises three successive layers: the input layer that contains the data to be processed, the output layer that performs the classification task, and, in the middle, a hidden layer that is the engine of any ANN computational procedure .The backpropagation algorithm is the most common ANN training algorithm, and it has two phases: feedforward and feedback. The feedforward phase determines the output, whereas the feedback phase calculates the error and updates the weights. By adjusting the weights, the error between the actual values and the ANN output is gradually reduced .This study used a feedforward backpropagation neural network with a sigmoid activation function. Additionally, a sigmoid function was used at the hidden layers to introduce nonlinearity to the network for learning and allowing the algorithm to generate complex mappings between the features and the outputs.The activation function often used in feedforward neural networks is the sigmoid function .The ANN model has 16 neurons in the input layer, two in the output layer, and 11 in the hidden layer. The appropriate number of hidden layer neurons is determined based on the experiment till achieving a sufficient error reduction, as there is no established theory for number setting yet. The optimal number of hidden layer neurons generates a better predictive neural network model. Tuning parameters included the number of nodes in the hidden layer optimized between 1 and 15. The threshold used was 0.07, and the learning rate was 0.001. Finally, the neurons in the input and the hidden layers were activated using the sigmoid function. The error function was cross-entropy. The error was 47.86. Nevertheless, it required 31,842 steps for convergence in 9.72 s.LR is a statistical method used to assess the relationship between a binary target variable and one or more predictors of any measurement level . To deteAll analyses were performed using R Statistical Software v.4.1.1 . CART wa


Patients at risk of harm from high-risk NSAID prescriptions are common in primary care. The elderly and patients on polypharmacy are at increased risk of having at least one positive PSI. Therefore, when NSAIDs are prescribed, it is recommended to weigh the benefits versus the risks for high-risk patients, such as the elderly and those with multiple-drug therapy.The quality and safety of prescribed drugs can be assessed using prescribing safety indicators (PSIs). This study aimed to estimate the prevalence of PSIs of oral non-steroidal anti-inflammatory drugs (NSAIDs) at primary care clinics of a tertiary care hospital in Saudi Arabia and to identify the risk factors associated with positive PSIs for patients. In this retrospective chart review study, data from the medical records of 450 patients aged ≥18 years, who were prescribed oral NSAIDs, were reviewed and collected manually. Seven PSIs were chosen and defined as follows: prescription of an oral NSAID to any patient with a peptic ulcer; aged ≥75 years; aged ≥65 years with a glomerular filtration rate <60; heart failure; co-prescribed warfarin; co-prescribed aspirin or clopidogrel; or co-prescribed angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker and a diuretic. Patients with positive indicators are at risk of harm from high-risk prescribing. The overall period prevalence of PSIs is 153/450 . The overall proportion of PSIs is 202/431 . The most common safety indicators were for NSAIDs prescribed to patients with heart failure and patients aged ≥65 years with a glomerular filtration rate <60. The elderly and patients using polypharmacy are at increased risk of having at least one positive PSI (OR 5.22; 95% CI 3.32–8.21,  Adverse events caused by medications are a major concern in all healthcare institutions and settings. The majority of hospital admissions are due to drug-related morbidity, which is generally the result of ineffective prescribing and monitoring during the medication management process .Some tools and methods can be used in the medication management process to analyse prescriptions, such as prescribing safety indicators (PSIs), which can assess the safety of medications in the prescribing stage; prescribing analysis and cost (PACT) data, which includes information on prescribing costs, the number of prescribed items, and generic prescribing; and the medication appropriateness index (MAI), which measures the appropriateness of prescribing for each medication for elderly patients ,3. The mPSIs were defined by Spencer et al. as “statements describing prescribing events that put the patient at risk of harm” . These iIn some cases, high-risk prescribing indicators are detected, but these indicators are not necessarily inappropriate . For exaIn this study, the focus is on indicators related to oral NSAIDs, only because the utilisation of NSAIDs is increasing through both self-use (over-the-counter (OTC) medication) and prescriptions from primary care clinics ,12. ConsTherefore, exploring and examining PSIs related to NSAIDs, which are a highly prescribed group of analgesics both locally and globally, will lay the groundwork for future studies aimed at developing interventions to prevent NSAID-related PSIs. Thus, the aim of this study is to estimate the prevalence of PSIs related to oral NSAIDs at the primary care clinics of a university-affiliated tertiary care centre in Saudi Arabia and to identify the risk factors of patients with positive PSIs related to oral NSAIDs, which is something that has not been examined before in Saudi healthcare settings.This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist and the Reporting of Studies Conducted Using Observational Routinely Collected Health Data (RECORD) statement see  20,21].,21.20,21A retrospective chart review study was carried out at the primary care clinics of a 1200-bed tertiary care hospital in the central region of Saudi Arabia. The hospital is a multi-disciplinary facility providing primary, secondary, and tertiary care, treating more than 1 million outpatients each year. In addition, approximately 45,966 patients are admitted and approximately 14,231 procedures are performed every year. The primary care clinics provide clinical management, prevention, surveillance and detection, and the maintenance of essential services.Ethical approval was obtained from the Institutional Review Board (IRB) for Health Sciences Colleges Research on Human Subjects, King Saud University, Riyadh, Saudi Arabia . The ethics committee waived the requirement of written informed consent for participation. All data and patient identifiers were fully anonymised . To ensuA pilot study was carried out with 10% of the total sample to ensure data collection and extraction feasibility. A list of all patients on oral NSAIDs who visited a primary care clinic from 1 January 2020 to 31 March 2020 (3-month period of the study) was generated from the electronic health record (EHR) system of the clinics, giving a list of 7836 deduplicated records. Each record was given a code number, and 1775 records were randomly selected using a random number table that was generated using the “simple random sample without replacement” function in STATA (version 14) statistical software. Screening for the eligibility and inclusion criteria was applied to 1775 records by reviewing each patient’s demographic information, medication list, and pre-existing diseases until a sample size of 450 patient records was reached. Finally, a serial number was assigned to each record , pre-existing diseases, pre-existing co-prescriptions  inhibitor, angiotensin II receptor blocker (ARB), antiplatelet agent, diuretic, or oral anticoagulant (OAC)), polypharmacy (≥5 medications at any point during the 12-week period) , and durPatients considered for inclusion were age 18 years or older, had active medical records, had been seen by a primary care provider at least 3 months prior to the start of data collection, were taking at least one oral NSAID, and had any co-morbid condition , diabetes mellitus, heart failure, hypertension, or osteoarthritis).Patients were excluded from the study if they were administering subcutaneous, intramuscular, or injectable NSAIDs, or if they took NSAIDs in an inpatient or emergency healthcare setting.a.Outcome variables:The PSIs measure the risk of drugs prescribed to vulnerable patients, a situation that involves the risk of harm. These drugs can put patients at risk of harm through possible gastrointestinal (GI) bleeding, renal toxicity, or worsening of a heart condition and potential failure because of the patient’s age (≥65 years), pre-existing condition , or co-prescribed drugs  see .Medications under antiplatelet, OAC or ACE inhibitor/ARB, and diuretic groups were medications available in the hospital formulary. In addition, gastroprotective drugs were those in the proton pump inhibitor and H-2 receptor antagonist groups.The PSIs were developed and validated based on Guthrie’s criteria for high-risk prescribing in general practice through a two-round consensus method using the Delphi process ,9,10. Inb.Risk factorsThe considered risk factors were age, gender, nationality, polypharmacy, and duration of NSAID use. Age, gender, and polypharmacy were evaluated as risk factors by Guthrie et al. and Stocks et al. ,23. Studc.ExposureIn this study, we considered exposure to one of the oral NSAIDs available in the hospital formulary: celecoxib, diclofenac, ibuprofen, indomethacin, and meloxicam. The data collection and extraction were undertaken manually on paper data collection sheets  at the pThe paper data collection sheet was designed by the research team to include the relevant information related to patient demographics, past medical history, co-prescribed drugs, and NSAID indicators. Data were then transferred to an electronic Excel datasheet and coded.A simple random model was employed for sampling in order to avoid selection bias. B.M.A. conducted the data collection. To ensure the reliability of the information, 20% of the data collection was double-checked by a second author (G.A.A.) ,28. In a(1)Retrospective chart review study sample size:The sample size was calculated based on the assumption that the prevalence of NSAID PSIs is 50%, because no similar studies have previously been conducted in Saudi Arabia . Therefo(2)Pilot sample size  : 10 × 38(3)Final adjusted sample size:The addition of 10–20% records was required to allow adjustment of other factors such as patients excluded for not meeting the inclusion criteria . The finN1 = N/(1 − 0.2) = 380/0.8 = 475 records to screen for eligibility.In this study, we screened 1775 records for eligibility because multiple records met the exclusion criteria and were eventually excluded.Final sample size: 450 records.To illustrate the respondents’ demographic characteristics, descriptive statistics were calculated using frequencies and percentages. The overall period prevalence rate was calculated as the number of patients experiencing one or more PSIs at any time during the 12-week period/total number of patients in study population ×100. The proportion of each PSI was calculated as the number of positive numerators for each PSI during the 12-week period/number of positive denominators for each PSI × 100.The overall PSI proportion was calculated as the total number of positive numerators for all PSIs during the12-week period/ total number of positive denominators for all PSIs × 100. For continuous variables, the results are presented as mean ± standard deviation. For categorical variables, the results are presented as counts with percentages.p < 0.05 and 95% CI were used. The analysis was conducted using STATA  statistical software.To evaluate the association of risk factors and outcome, logistic regression analysis was conducted. The dependent variable was the presence/absence of the outcome (PSI). The patient- and medication-related independent variables were age in years, gender, nationality, polypharmacy (≥5 medications), and duration of NSAID use. All independent variables were binary , except for the duration of NSAID use, which had 5 categories . Significance at The Excel data sheet was checked for outliers or errors in data entry. For the PSI outcomes, several data checks were used to ensure the following: the ages of included patients met the inclusion criteria for the relevant PSI outcome; the co-prescribed drugs met the criteria for the relevant outcome measure; the co-existing conditions met the criteria for the relevant outcome measure; cases labelled as numerators met the criteria for being numerators; and cases labelled as denominators met the criteria for being denominators.Patients and the public were not involved in the designing, carrying out, reporting, or disseminating of the plans of this research.A total of 450 records met the inclusion criteria and were included in the retrospective chart review study see .The demographic characteristics show that 64% of patients were between 18 and 64 years old, with a mean age of 62.01 years (standard deviation (SD) 11.15), and that 70% were female. All patients were Saudi. Most of the patients (90%) were using five or more prescription medications. Celecoxib 200 mg was the most used NSAID in this study population (53%). None of the patients were on indomethacin. More than 60% of patients had one of the following conditions: diabetes mellitus, dyslipidaemia, or hypertension .NSAIDs were prescribed for arthritic conditions and different types of pain in the shoulders, back, joints, and muscles, as well as migraine headaches and fibromyalgia.The overall period prevalence rate was (153/450) × 100= 34% (95% CI 29.60–38.39). The PSIs with the highest proportions were number 6, NSAID prescribed for patients over 65 with an estimated glomerular filtration rate <60, and number 7, NSAID prescribed for patients with heart failure, both at 100%. The overall proportion of PSIs was (202/431) × 100 = 46.9% (95% CI 42.12–51.61) .p-value < 0.001).Patients ≥ 65 years of age were estimated to be five times more likely to have at least one positive PSI than those aged 18–64 years . Patients using polypharmacy were estimated to be approximately three times more likely to have at least one positive PSI than those not using polypharmacy . The overall proportion of PSIs is 46.9% (95% CI 42.12–51.61). This is higher than the proportion identified in a UK study, which found that their composite indicator, defined as “prescription of an NSAID to a person with any peptic ulcer, aged 75 years or over, heart failure, co-prescribed warfarin or co-prescribed aspirin or clopidogrel”, varied between general practices ranging from 4.1 to 21% . Female Comparing our results with those of PSIs studies conducted on general practices in the UK, the proportion of patients identified at risk for each PSI was higher in our study for PSI 2, NSAID prescribed to a person aged ≥75 years ; PSI 3, A previous study investigated an area similar to that of the current research: a retrospective cohort study was conducted in Saudi Arabia in 2019 to investigate clinically important prescription and monitoring errors among adults managed in family medicine clinics . HoweverThis study has several identifiable strengths. The list of PSIs used in this study was generated and validated using the consensus method to ensure the proper use of medications in primary care ,10. RandThe limitations of this study are as follows: first, the results of this study cannot be generalised to the primary care setting in Saudi Arabia since it is a single-centre study. Second, there is a risk of information bias, as the investigators relied on only EHR information for the identification and assessment of the PSIs. Third, we were not able to see if being Saudi or not was associated with having any PSIs, because none of our patients were non-Saudis. Fourth, the data collection in this study was restricted to a 3-month period, which was suitable for the timetable of the master project and in line with the COVID-19 pandemic, so many factors could have affected our results, such as seasonal variations in prescribing and variations between clinical settings in the region. These two factors were not evaluated in this study; further studies are required to comprehensively assess such factors and PSIs in primary care. More research could be conducted to replicate this study in different regions of Saudi Arabia in order to enable generalisation of the results. More research is also needed to understand the patient perspective on the risks and benefits of high-risk NSAID prescribing. Educational workshops for pharmacy students and primary care and community pharmacists are needed to ensure an understanding of indicators showing that patients may be at risk of harm. In addition, optimising those indicators for the electronic record system to alert physicians in case of positive indicators and prevent exposing patients to the risk of harm should be made a priority ,33.In the case of patients buying NSAIDs as self-medication from a community pharmacy or physicians prescribing them in primary care, pharmacists and/or prescribing physicians must ask about any pre-existing conditions or co-prescribed drugs that could increase the risk of harm. Once apprised of that information, they should provide advice regarding the benefits versus risks and counsel patients in order to ensure the safe and proper use of medications. In addition, the active role of patients taking responsibility to ensure the safety of their self-care treatment should not be ignored. This goal could be met by facilitating the implementation of online portals that allow patients to access their own medical records in order to enhance their communication with healthcare providers ,35.The use of oral NSAIDs for high-risk prescribing in primary care is common. The overall period prevalence of PSIs related to oral NSAIDs is higher than expected, particularly among elderly patients and those with polypharmacy. It is highly recommended to weigh the benefits and risks of using oral NSAIDs in order to ensure the safe and proper use of these medications.


Whether heart failure (HF) is an independent risk factor of ischemic stroke (IS) and hemorrhagic stroke remains controversial. We employed a multivariable Mendelian randomization (MR) to further investigate the causal effects of HF on the risk of stroke and stroke subtypes.Genetically predicted HF was selected as an instrumental variable (IV) from published genome-wide association studies (GWAS) meta-analyses. Stroke data with different etiologies were extracted as outcome variables from another two GWAS meta-analyses. The random-effects inverse variance-weighted (IVW) model was applied as the main method, along with sensitivity analysis. Atrial fibrillation (AF), coronary heart disease (CHD), and systolic blood pressure (SBP) were controlled for mediating effects in multivariable MR.p = 0.03], large artery stroke , and cardioembolic stroke , but without small vessel stroke  and intracerebral hemorrhage  in univariable MR. However, these significant associations were attenuated to the null after adjusting for confounding factor in multivariable MR.Genetically predicted HF was significantly associated with any IS [odds ratio (OR), 1.39; 95% CI, 1.12–1.74; There was no direct causal association between HF and stroke in our study. The association between HF and IS can be driven by AF, CHD, and SBP. Stroke is now the leading cause of global mortality and disability, with an increasing global burden . IschemiEvidence from observational studies is limited by residual confounding and detection bias . Stroke r2 ≥ 0.8) were used for those unavailable in the stroke database  with the stringent pairwise linkage disequilibrium R2 < 0.001 from a published genome-wide association studies (GWAS) meta-analysis. This GWAS meta-analysis pooled 26 studies, i.e., 47,309 HF patients and 930,014 controls from the Heart Failure Molecular Epidemiology for Therapeutic Targets Consortium , SVS , and LAS . IS was defined as a rapidly developing sign of focal  neurological deficit enduring at least 24 h or until death with no obvious cause other than that of vascular origin  and the International Consortium of Blood Pressure  database. The mean age of participants ranged from 56.8 to 62.1 years old with a mean SBP of 138.4 mm Hg , and the Michigan Genomics Initiative (MGI), DiscovEHR] with a total of 60,620 cases and 970,216 controls of European ancestry . The dia.4 mm Hg . All datp < 0.05) were removed in the subsequent MR analysis. The IVW method is equivalent to a weighted regression of SNP-outcome effects on SNP exposure, with the y-axis intercept fixed to zero  with more IVs was provided as a sensitivity analysis to estimate the direct causal association between HF and stroke. Statistically significant findings (p < 0.05) of univariable MR analysis were further investigated in subsequent multivariable MR analysis by adjusting for AF, CHD, and SBP. For multivariable MR, the multiplicative random-effects IVW method was performed as the primary analysis method, a Bonferroni correction threshold  was used to ascertain statistical significance. The strength of the genetic instrument was tested using F-statistics  meta-analysis was conducted as the primary analysis to estimate the associations between HF and stroke subtypes . The MR  to zero . Conside to zero . In the  to zero . The inteiotropy . Generaleiotropy . A genomatistics . F-statiatistics . All staatistics .p = 0.03], LAS , and CES . We found no causal association of genetically predicted HF with SVS  or ICH . However, the causal association between HF and LAS, CES and any IS was no longer statistically significant after adjusting for CHD, AF, and all of the three confounders, respectively, in a multivariable MR analysis . Significant heterogeneity was shown in the association of HF with any IS, LAS, and CES from the MR-PRESSO global test  as a sensitivity analysis validated the consistent results  as the sensitivity analyses. As expected, our study confirmed that the effect of HF on CES depended on AF, the effect of HF on LAS depended on CHD, a proxy for systemic atherosclerosis, the effect of HF on any IS depended on a comprehensive risk factor involving AF, CAD, and SBP. However, some previous observational studies regarding HF as an independent risk factor on IS remain controversial. Given that, stroke and HF are heterogeneous diseases and share many common cardiovascular risk factors, which may be easy to bias the results in observational studies. Inconsistency with our findings, a recent MR study found that HF was an independent risk factor of any IS after adjusting for a single cardiovascular risk factor  were not completely in line with the IVW method in the univariable MR analysis. HF and stroke are heterogeneous diseases, and we were unable to fully rule out the possibility of pleiotropy in this situation. However, most estimates were consistent with sensitivity analysis, which indicates that the observed associations are robust and are not likely to be by chance observations. Second, we were limited by the fact that MR analyses usually evaluate the effects of life-long exposure on the outcomes and thus might be biased by the effects of clinical interventions with shorter periods of exposure. The effect sizes estimated in this study were limited to explaining the associations between critical periods of exposure and outcomes . Third, In conclusion, at genetic levels, we found no evidence of a direct causal association between HF and stroke and its subtypes. Our study highlights the importance of controlling for confounding biases in exploring the genetic correlation between two heterogeneous diseases, such as HF and stroke.https://gwas.mrcieu.ac.uk/.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: CL and HZ conceived and designed the study. QL analyzed the data and drafted the manuscript. SY participated in the analysis of data and revised the manuscript. YL and HK prepared all the figures. All authors read and approved the final manuscript.This study was supported by CAMS Innovation Fund for Medical Sciences (2019-I2M-5-023) and Hainan Provincial Key Research and Development Project (ZDYF2020112).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Dorea, Streptococcus, and g_Eryipelotrichaceae had a high correlation with obesity and obesity-related metabolic disorders of the host. Therefore, it can be seen that this new MLCT has different properties and functions from the previous traditional MLCT, and it can better combine the advantages of MLCT, lauric acid, and sn-2 palmitate, as well as the advantages of health function and metabolism. In summary, this study explored the effects of LaPLa-enriched lipids on glycolipid metabolism in mice, providing theoretical support for future studies on the efficacy of different types of conjugated lipids, intending to apply them to industrial production and subsequent development of related products.The roles of medium- and long-chain triacylglycerols (MLCT) on health benefits under high fat diet (HFD) conditions remain in dispute. This study was conducted to investigate the effects of novel LaPLa-rich MLCT on the glycolipid metabolism and gut microbiota in HFD-fed mice when pork fat is half replaced with MLCT and palm stearin (PS). The results showed that although MLCT could increase the body weight in the mouse model, it can improve the energy utilization, regulate the glucose and lipid metabolism, and inhibit the occurrence of inflammation. Furthermore, 16S rRNA gene sequencing of gut microbiota indicated that PS and MLCT affected the overall structure of the gut microbiota to a varying extent and specifically changed the abundance of some operational taxonomic units (OTUs). Moreover, several OTUs belonging to the genera  Medium- and long-chain triacylglycerols (MLCT), which always involve essential fatty acids, are structural lipids containing both medium chain triacylglycerols (MCT) and long chain triacylglycerols (LCT) in triglyceride molecules . The metWith the improvement of social living standards, and the changes in diet and lifestyle, the incidence of obesity has increased, and the related diseases caused by obesity have been paid more attention. In 2005, the number of obese and overweight people worldwide reached 1.3 billion, and the figure is expected to increase to 2 billion by 2030 . ImportaThe research and development of MLCT was initially aimed at people with lipid metabolism disorders and assisted in the improvement of obesity caused by excessive intake of fat, as well as various complications . Along wThe gut microbiota is now considered to be a key factor in metabolic health. Different dietary lipid compositions have different effects on gut microbiota structure, and an unbalanced dietary lipid structure may lead to intestinal dysbiosis, which may lead to intestinal inflammatory diseases . High-faIn this study, 50% of the lard in the high-fat diet was replaced by MLCT as the experimental group, and the high-fat diet group (HFD), basic diet group (NCD), and palm stearin group (PS) were set as the control group. It is consistent with the replacement ratio of MLCT feed lard. In this way, it is a preliminary exploration of the function of a novel MLCT, to study the effect on the lipid metabolism of mice, to provide a theoretical basis for evaluating the function of a new type of lauric acid-rich MLCT, and to lay a foundation for the subsequent development of similar products.As shown in After 10 weeks of feeding, there was a significant increase in the percentage weight of iWAT and eWAT in the HFD and MLCT groups A. In comGenerally, the changes in serum lipids are closely related to the body weight. The level of lipid metabolism is reflected by the four serum lipids  . Figure Obesity is always accompanied with glucose metabolism disorders and insulin resistance. In the case of GTT, Leptin and adiponectin are two important lipid metabolism regulators. As identified in The glutamic-pyruvate transaminase (GPT) and glutamic-oxalacetic transaminase (GOT) activities are positively related to the inflammatory status, especially the occurrence of liver disease. Lipopolysaccharide (LPS) is an endotoxin that causes inflammation. Lipopolysaccharide binding protein (LBP) is a reactive protein whose content increases in response to LPS. In addition, inflammation is always accompanied by high levels of serum alkaline phosphatase (ALP) . The resWe investigated whether MLCT can exert a healthy effect on weight gaining by affecting the composition of the gut microbiota. The Sobs and Chao1 indices were decreased in HFD, PS, and MLCT compared to the NCD group A,B, whilg_unclassified_Erysipelotrichaceae, Olsenella, Dorea, Helicobacter, Acetatifactor, unclassified_p__Firmicutes, Anaerovorax, Romboutsia, Lactococcus, and Parvibacter, and significantly decreased the relative abundance of unclassified_Porphyromonadaceae, Turicibacter, Barnesiella, unclassified_Coriobacteriaceae, Alloprevotella, and norank_f__unclassified_Clostridiales compared to the NCD group. To further examine the specific effects of half replacement of PS and MLCT on the gut microbiota in HFD-fed mice, an LEfSe analysis was used to find the distinct bacteria in each group (unclassified_Porphyromonadaceae and in the HFD-fed mice was g_unclassified_Erysipelotrichaceae (g_unclassified_Erysipelotrichaceae) were significantly decreased after PS and MLCT intervention . Contradictory to our experiments, in terms of body weight gain for the mice subjects, most studies showed MLCT did not significantly affect the body weight gain. It was suggested that the effect of MLCT on body weight might be influenced by the dietary fat content or by energy sufficiency [p > 0.05; p > 0.05), which determines that the energy intake and the energy conversion rate of the MLCT group is higher than other groups.The present study was carried out to evaluate the effect of MLCT on body weight, glycolipid metabolism, and gut microbiota composition for 10 weeks in half replaced dietary fat conditions. There was a trend that the body weight was found to be the highest in LaPLa-enriched MLCT fed mice  mainly achieves effective regulation of body weight by balancing energy and suppressing appetite. Adiponectin (ADP) plays a protective role in diabetic nephropathy (DN) through the adiponectin receptor, mediates fatty acid metabolism, protects and compensates for further kidney damage, and prevents the development of clinical nephropathy . The resIn the following study, MLCT was found to have great influence on the overall structure of the gut microbiota, and the influence extent depended on the composition and dosage of MLCT. As expected, the ratio of Firmicutes to Bacteroidetes (F/B) in the HFD group was notably higher than the ratio in the NCD group . This isg_Eryipelotrichaceae and Desulfovibrio, which were the characteristic flora of the HFD group, were found to be positively correlated to the TCHO, suggesting that these bacteria may play a lipid-absorption role in the development of obesity [Dorea  fed with different treatment diets. During the experiment, the mice could free access to food and water, feeding for 10 weeks. The specific experimental group design is as follows:(1) High-fat diet group (HFD), fed with 206.8 g/kg lard in diet.(2) The MLCT group, fed with 103.4 g/kg MLCT and 103.4 g/kg lard in diet.(3) The palm stearin group (PS), fed with 103.4 g/kg PS and 103.4 g/kg lard in diet.(4) The normal control diet group (NCD), fed with the basic diet.Body weight and feed intake were regularly recorded every week. On the 9th week, the mice were fasted for 12 h and tested for glucose tolerance. After 10 weeks, the mice were fasted overnight, and fresh blood samples were collected from the orbit. Then cervical dislocation was sacrificed. The organs and tissues such as liver, epididymal white fat tissue (eWAT), inguinal white adipose tissue (iWAT), small intestine, and brown fat tissue (BAT) were dissected and collected. Except for intestinal tissues, the rest of tissues were quickly weighed and recorded. The liver was fixed in 4% paraformaldehyde tissue for a fixative solution, and some eWAT were fixed in fat fixation solution for follow-up morphological analysis. The remaining tissues were quick-frozen in liquid nitrogen and stored at −80 °C for subsequent analysis.The animal experiment was approved by the Animal Ethics Committee of Zhejiang University of Traditional Chinese Medicine , and all experimental procedures were in accordance with the animal welfare regulations of Zhejiang University of Traditional Chinese Medicine.−1 body weight, and blood samples were taken for 30 min, 60 min, 90 min, and 120 min for blood glucose measurement. The glucose curve was drawn, and the area under the curve (AUC) was calculated according to the approximate trapezoidal method.The glucose tolerance test is namely the GTT test. On the 9th week of the feeding experiment, the fasting blood glucose of the mice was measured by taking blood from the tail vein after the mice were fasted for 12 h, which was recorded as 0 min blood glucose. Then 20% glucose solution was injected intraperitoneally at the dose of 2 g kgThe blood of mice was collected and stood at room temperature for 30 min, then centrifuged at 3000 rpm for 20 min. The upper serum was collected and placed in a sterilized 2.5 mL centrifuge tube every 50 μL. The samples were labeled and stored in a refrigerator at −80 °C for testing. The contents of glucose, TG, TCHO, HDL-C, LDL-C, GPT, and GOT in serum were determined according to the operation steps of Nanjing Jiancheng kit. While the contents of insulin, LPS, ALP, LBP, LEP, and ADP in the serum were determined according to the operating procedures of Genemei ELISA kit.HOMA-IR) is calculated according to the following formula:The insulin resistance index , and hematoxylin-eosin (H&E) staining were performed. After dehydration and mounting, pathological observation and analysis of tissue sections were performed under an optical microscope . Five fields of view were randomly selected for each section for photographing. Finally, the area of adipocytes in epididymis was statistically analyzed by ImageJ software .For gut microbiota analysis, the procedure was carried out according to our study . Brieflyp < 0.05. The OriginPro 8.0 and GraphPad Prism 6.0 were used for graph analysis. The differences between multiple groups were compared by using Tukey’s test in One-way ANOVA or Two-way ANOVA test for analysis of variance and significance. p < 0.05 was considered a significant statistical difference, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.The data obtained from the experiment was analyzed by SPSS Statistics, and the significant difference was declared at Overall, this study strengthens the idea that the MLCT had a faster weight gain effect, compared with the high-fat diet, had no significant difference in lipid metabolism, and had better regulation of glucose homeostasis than the HFD group. This characteristic is different from the traditional MLCT, which provides a reference for the study of the efficacy of MLCT synthesized from lauric acid. At the same time, it is good functionally for people who need to gain weight quickly and replenish energy with a good application prospect in animal husbandry. In addition, the insulin tolerance test and mechanism of anti-inflammatory action can be analyzed in the future in order to elucidate how MLCT can produce anti-inflammatory activity under the condition of reducing insulin resistance. Besides, other types of MLCT can be further synthesized according to the different lengths of carbon chain to explore fatty acids with more functions.


Moreover, there is a lack of reproducibility and standardization in research in the role of such measurements in cardiovascular risk. In addition, there is limited understanding of how such measurements may vary between populations. In the current Topic Research, we collected 6 high quality studies to highlight novel epidemiological research, in different population groups in order to examine the correlation between the CVD risk and renal biomarkers.Chronic kidney disease (CKD) is a leading risk factor in the development of cardiovascular disease (CVD) and is associated with higher risk of mortality . TherefoLiang et al. performed a meta-analysis for the prognosis in patients with heart failure, and they found a greater risk of all-cause mortality associated with dipstick proteinuria [HR: 1.54 ], microalbuminuria  of 30 - 299 mg/g) [HR: 1.54 ] and macroalbuminuria (urine ACR ≥ 300 mg/g) [HR: 1.76 ]. In another paper, Park et al. performed an insurance data-registered longitudinal study in 124,691 Korean adults, and transient trace albuminuria was defined by a presence of trace (±) reaction on a dipstick test performed in the 2005/2006 or the 2007/2008 visit. The results were consistent to the findings of the meta-analysis performed by Liang et al. that those with transient trace albuminuria may have a greater risk of all-cause and CVD mortalities . According to these study findings, albuminuria of any degree can be considered as a useful biomarker and predictor of mortality.With regard to albuminuria and dipstick proteinuria, Russo et al. studied the independent impact of SUA and CKD on mortality using the Uric Acid Right for Heart Health (URRAH) project (2) was higher in patients with hyperuricemia (SUA ≥5.9 mg/dL) and albuminuria  as compared to those with only one risk factor or none . In addition, for each SUA increase of 1 mg/dL, the risk for all-cause mortality increased by 10% even after adjustment for potential confounders including eGFR and the presence of albuminuria. In another study using the China Health and Nutrition Survey (CHNS) conducted by Li et al., hyperuricemia was defined as 7.0 mg/dL or greater for men and 6.0 mg/dL or greater for women. CVD risk was graded using the Framingham risk score (FRS) as low (FRS < 10%), medium (FRS 10–20%), or high (FRS >20%). The study uncovered a negative multiplicative interaction between hyperuricemia and CVD risk on CKD. The odds ratio (OR) between hyperuricemia and CKD in the low-CVD risk group was 5.51 , followed by 3.64  in the medium-CVD risk group and 2.89  in the high-CVD risk group. Moreover, hyperuricemia and age had an additive effect on CKD, with a synergy index of 2.26 . Based on these study findings, there were significant interplays between SUA, CKD and CVD risk, and the role of hyperuricemia as a crucial biomarker to predict CKD and CVD was emphasized.With regard to serum uric acid (SUA) and CKD which were mutually affected , we coll project , and revWei et al. firstly examined the association between urinary MGP and all-cause mortality in 776 randomly recruited Flemish subjects. For a doubling of urinary MGP, the risk of all-cause and CVD mortalities was found to increase  with adjustment for covariates including eGFR and microalbuminuria. The study was consistent to the previous reports that both circulating and urinary MGP could be utilized as a useful biomarker to predict mortality (For the matrix Gla protein (MGP) , a smallGao et al. investigated the association of eGFR with CVD and all-cause mortalities in 2,366 old-aged Chinese patients with acute myocardial infarction (AMI) who were rarely documented , 0.61 , and 0.48 , respectively] and all-cause mortality . The studies highlighted the role of eGFR on mortality even in the elderly population.Finally, cumented . The stuTaken together, the present Research Topic represents an important source of up-to-date information, covering many aspects of renal function and related biomarkers in assessment and prevention of CVD events. More comprehensive knowledge according to these discoveries may bring about new perspectives.Both authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.