Women with diminished ovarian reserve (DOR) have a lower pregnancy rate and higher cancellation rate compared to those without DOR when seeking assisted reproductive technology. However, which factors are associated with reproductive outcomes and whether AMH is a predictor of clinical pregnancy remain unclear.This retrospective study was designed to find factors associated with reproductive outcomes in DOR patients and then discuss the role of AMH in predicting cycle results among this population.A total of 900 women were included in the study. They were diagnosed with DOR with the following criteria: (i) FSH > 10 IU/L; (ii)AMH < 1.1 ng/ml; and (iii) AFC <7. They were divided into different groups: firstly, based on whether they were clinically pregnant or not, pregnant group vs. non-pregnant group (comparison 1); secondly, if patients had transferrable embryos (TE) or not, TE vs. no TE group (comparison 2); thirdly, patients undergoing embryo transfer (ET) cycles were divided into pregnant I and non-pregnant I group (comparison 3). The baseline and ovarian stimulation characteristics of these women in their first IVF/ICSI cycles were analyzed. Logistic regression was performed to find factors associated with clinical pregnancy.Of the 900 DOR patients, 138 women got pregnant in their first IVF/ICSI cycles while the rest did not. AMH was an independent predictor of TE after adjusting for confounding factors . Further ROC (receiver operating characteristic) analysis was performed and the corresponding AUC (the area under the curve) was 0.679 . Notably, an AMH level of 0.355 had a sensitivity of 62.6% and specificity of 65.6%. However, there was no statistical difference in AMH level in comparison 3, and multivariate logistic regression showed female age was associated with clinical pregnancy in ET cycles and women who were under 35 years old were more likely to be pregnant compared to those older than 40 years old .AMH is highly related to oocyte collection rate and TE rate,and 0.355 ng/ml was a cutoff value for the prediction of TE. For DOR patients who had an embryo transferred, AMH is not associated with clinical pregnancy while female age is an independent risk factor for it. Diminished ovarian reserve (DOR) refers to the reduction of the quantity of oocytes in the ovary, which is one of the major causes of infertility in women of child-bearing age . OvarianBased on the Bologna criteria , folliclControversy has existed on whether AMH is associated with reproductive outcomes in assisted reproductive technology (ART) cycles. A meta-analysis reviewed 19 articles including unspecified ovarian reserve, DOR, and polycystic ovary syndrome (PCOS) patients. Results showed that AMH had weak association with clinical pregnancy but could be a predictor in DOR women . A studyIn this retrospective study, we collected data from the first IVF/ICSI cycles of patients with DOR and analyzed baseline and controlled ovarian stimulation (COS) characteristics to find out factors associated with reproductive outcomes and then discuss the role of AMH in predicting cycle results among this population.Patients who came to the First Affiliated Hospital of Zhengzhou University for autologous IVF/ICSI cycles were enrolled into this study during January 2011 to December 2019. Patients who have fulfilled the following criteria were included: (i) FSH > 10 IU/L and AMH < 1.1 ng/ml and AFC <7; (ii) the first fresh IVF/ICSI cycle in our center. While the participants who had (i) endometriosis, polycystic ovarian syndrome (PCOS); (ii) chromosomal abnormalities; (iii) hypertension, diabetes, or other chronic diseases; (iv) immune system diseases, such as hypothyroidism; (v) multiple uterine fibroids or a history of ovarian surgery or chemotherapy or radiation exposure; (vi) experienced IVF/ICSI cycles at other hospitals; (vii) premature ovarian insufficiency were excluded  to detect AMH levels (ng/ml). The theoretical sensitivity of the method was 0.006 ng/ml. Within batches and between batches coefficient of variations were ≤10% and ≤15%, respectively.The ovarian stimulation protocol was determined by the ovarian reserve testing  of each patient.Medroxyprogesterone acetate and human menopausal gonadotropin (HMG) were used on the third day of menstruation, human chorionic gonadotropin (HCG) was used at the time of triggering.Patients were given a starting dose of 3.75 mg GnRH agonist (GnRH-a) on the second day of menstruation. Gonadotropins (Gn) were used to induce ovulation, and we adjusted the dose according to the number, size, and growth of the follicles.Letrozole was given to patients at a dose of 2.5 mg per day on the third day of menstruation, and HMG was added on the fifth day. Once the diameter of a primary follicle was > 18 mm, HCG and Gn were used.The number, size, and growth of follicles and hormone levels, especially LH, E2, and P4, were observed during menstruation to determine the time of triggering.GnRH-a was used on the 21st day of menstruation, and ultrasound and hormone levels were used to observe the growth of the follicles. Ovulation was induced using HCG according to the size of the follicles.FSH was given to patients on their second day of menstruation, and Gn and HCG were injected when the diameter of the primary follicle was > 18 mm.In the above protocols, HCG was used for 36-37 h before oocyte retrieval. IVF or ICSI was used according to the semen quality of the husband.The baseline and ovarian stimulation characteristics of patients were compared between each two groups (the grouping method was as described above). Continuous variables were compared by Mann–Whitney U since they were not normally distributed. Chi-squared tests were used to compare categorical variables. The numerical data are presented as the mean with standard deviation (SD), while categorical variables are shown as % (n/N). We performed the univariate and multivariate logistic regression analyses to examine factors that were associated with reproductive outcomes. IBM SPSS version 26.0  was used, and a P value < 0.05 was considered statistically significant.Of the 900 patients who met the criteria in the study, 778 (86.44%) women had oocytes retrieved at their first cycle, 551 (61.22%) women had transferrable embryos after egg collection, 139 (15.44%) patients had embryos frozen, and 138 (15.33%) women got pregnant after implantation.As shown in In terms of transferrable embryo, only age of female , basal FSH levels , basal AMH levels , and AFC  differed between TE and no TE group. Others were not significantly different.In comparison 3, FSH  and AMH  had no statistical difference between pregnant I and non-pregnant I group, while AFC  was at the threshold value of P < 0.05. Both maternal  and paternal  age differed significantly between the two groups.As shown in In comparison 2, the usage of Gn, hormone levels on the day of HCG administration, type of ART, and the choice of stimulation protocol were different between two groups. Due to the difference of protocol choice, more dosage and days of Gn were used in TE group (P < 0.001).In ET cycles, all patients underwent embryos implantation, number of oocytes  and embryos (P < 0.001) differed significantly between pregnant and non-pregnant groups; hormone levels, Gn usage, embryo stage, and type of ART had no significant difference.To find which factors were associated with reproductive outcomes in women with DOR, univariate and multivariate logistic regression analyses were performed Table 3.Model 1 included factors associated with TE, and results showed that AMH was an independent predictor of TE after adjusting for confounding factors . Further ROC (receiver operating characteristic) analysis was performed and corresponding AUC (the area under the curve) was 0.679 . Notably, AMH level of 0.355 had sensitivity of 62.6% and specificity of 65.6%. Figure 2Model 2 included factors associated with clinical pregnancy in ET cycles. Female age was the only factor associated with clinical pregnancy in ET cycles and women under 35 years old were more likely to be pregnant compared to those older than 40 years old .in utero. However, it begins to decline before the time of birth and continues to decline throughout the fertile years  of these women, appropriate stimulation protocol was selected to avoid the adverse reactions of high-dose exogenous hormones and reduce the economic burden on patients. In COS cycles, patients in the non-pregnant group tended to use GnRH antagonist protocol and Follicular phase long-acting protocol. Natural protocol and mild stimulation protocol were in the non-pregnant group only. Correspondingly, women in non-pregnant group used much lower Gn and got fewer oocytes and embryos. Published data compared various protocols in DOR patients, GnRH agonist protocol and modified natural cycle were thought to be more effective than other protocols, since they could improve the quality of oocytes and probability of live birth of women with DOR , 21. TheProduced by developing antral follicles in the ovaries and involved in the regulatory process of maturation of primordial follicles, AMH is considered an accurate biomarker to access ovarian reserve and ovarian response , 23. RecFollicles in female ovarian apoptosis and the decrease with increasing of age means the capacity of fertility is dropping over time, therefore, age can largely determine whether conception can be successful. Studies have shown that in DOR patients, younger women have higher pregnancy rate and lower miscarriage rate compared to their older peers , 26, 27.This study has some strengths. First, due to the rigorous definition of DOR, the homogeneity of the patients included was high, and possible confounding factors were removed. Second, we divided the patients into different groups step by step, and deeply explored the relevant factors related to the fecundity of DOR patients. This grouping method is conducive to controlling the influence of confounding factors.There were also several limitations in our study. One is the nature of the retrospective study. Data from a single center also weakened the reliability. In addition, we only included the first IVF/ICSI cycle of these patients; CLBR were not analyzed. Conception, not live birth, was our main outcome, while live birth is crucial for accessing fecundity. Therefore, the conclusions from this study are not definitive but indicative, and these findings need to be confirmed by more prospective and multi-center studies.AMH is highly related to oocyte collection rate and TE rateand 0.355 ng/ml was a cutoff value for the prediction of TE. For DOR patients who had embryo transferred, AMH is not associated with clinical pregnancy while female age is an independent risk factor for it.The original contributions presented in the study are included in the article/This study was performed under institutional review board approval of the First Affiliated Hospital of Zhengzhou UniversityLL and BS: designed study, analyzed data, and drafted the manuscript. FW and YZ: reviewed the manuscript. YS: Study conceptualization and review. All authors approved the final manuscript.This study was supported by Henan Province Medical Science and Technology Research (Key) Project (FDN-SBGJ202002049); Henan Province Medical Scienceand Technology Research Project (FDN-LHGJ20210353).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.


Our study contributes to the literature by being among the few to rely on objectively recorded smartphone usage data and utilizing components of trait EI as predictors.Most research on Problematic Internet Usage (PIU) relied on self-report data when measuring the time spent on the internet. Self-reporting of use, typically done through a survey, showed discrepancies from the actual amount of use. Studies exploring the association between trait emotional intelligence (EI) components and the subjective feeling on technology usage and PIU are also limited. The current cross-sectional study aims to examine whether the objectively recorded technology usage, taking smartphone usage as a representative, components of trait EI , and happiness with phone use can predict PIU and its components . A total of 268 participants  reported their demographic and completed a questionnaire that included Problematic Internet Usage Questionnaire short form (PIUQ–SF–6), Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), level of happiness with the amount and frequency of smartphone use, and living conditions . Their smartphone usage was objectively recorded through a dedicated app. A series of one-way ANOVA revealed no significant difference in PIU for different living conditions and a significant difference in the subjective level of happiness with phone usage  = 7.55, p < .001), as well as of the frequency of usage where the unhappy group had higher PIU  = 6.85, p < .001). Multiple linear regression analysis showed that happiness with phone usage (β = −.17), the actual usage of communication (β = .17), social media (β = .19) and gaming apps (β = .13), and trait EI component of self-control (β = −.28) were all significant predictors of PIU. Moreover, gender, age, and happiness with the frequency of phone usage were not significant predictors of PIU. The whole model accounted for the total variance of PIU by 32.5% (Adjusted R Problematic interenet usage; Smartphone apps usage; Trait emotional intelligence; Internet addiction; Addictive behavior. From 2000 to 2021, internet use increased by 1,331.9%. In early 2020, global internet traffic increased from 20% to 70%  and can Internet overuse and its associated psychological problems were proposed in 1995 as an Internet Addiction Disorder . AlthougExtensive research has studied the factors associated with PIU and the process of its development, which shall help devise measures that can reduce its impact and improve people's lives. For example, a study conducted at a Canadian university revealed several parameters that can predict PIU, including a low level of well-being, gender, marital status, ethnic background, and heavy internet use for communication purposes . Other sEarly theories compared PIU to substance use addiction and suggested the time spent on the internet as a diagnostic criterion for PIU , 30. TheEmpirical studies using objective data to investigate the relationship between internet use and PIU started to appear. The amount of studies that utilized this kind of approach to measure screen time is still limited , 45. RecEmotional Intelligence (EI) has been defined as the ability to regulate emotions in one's self and others, employ feelings to plan, guide actions and achieve in life , 50. On (RQ1): Does PIU differ according to the different categories of age, gender, and living condition?(RQ2): Can levels of subjective happiness with phone use, objectively measured amount of smartphone usage of each category of apps, and the four components of EI predict PIU?(RQ3): Can levels of subjective happiness with phone use, objectively measured amount of smartphone usage of each category of apps, and the four components of EI predict the three components of PIU?In this study, we investigate the relationship between the use of each type of applications, on the one hand, and PIU and its components, on the other hand. Participants' actual usage was collected objectively via an app that automatically collects smartphone usage and is already available on Google Play. Due to the ease of access to the internet through smartphones and the high percentage of internet users who access the internet via their smartphones, smartphone usage is considered a representative of technology usage, particularly non-essential usage, which is the main concern of PIU. It has also been shown that time spent on smartphones and time spent on the internet correlate . We then22.1The data were collected objectively by monitoring the user's smartphone screen time and recording the usage via a dedicated smartphone application. The application explains in its privacy policy, to which all users agreed, its data collection process and that it shares data anonymously with academics and research partners. Nevertheless, during application installation, the users were explicitly asked if they agree to participate. The users who participated in the survey were offered a premium version of the application. A total of 602 users responded to the survey and agreed to take part in our study. Participants who had incomplete responses to the questionnaires of the current study or answered in quick time were excluded. Those who used the application for less than seven days and those for whom we had missing data in these first seven days were excluded. Only 268 participants  met the inclusion criteria. Our participants were from 10 countries . Their ages ranged from 15 to 64, and the age question was answered using age range response options . For this study, the ages were grouped into the emerging adults group, those in the age range of 15–24 as defined by the United Nations , and the2.22.2.1Unhappy with it, 2: Somewhat unhappy with it, 3: Somewhat happy with it, and 4: Happy with it.The collected demographic information included gender, age, and living condition, specifically whether the participant is living alone, with a partner or family, with roommates, or prefers not to say. The data also captured the participant's level of happiness with phone use by using two items. The first item measured happiness with phone usage by asking the participants how they felt about the amount of time they used their phones per day. The second item measured happiness with the frequency of phone checking by asking the participants how they felt about the number of times they checked their phone per hour. The two items were on a four-point scale: 1: 2.2.2never” to “always/almost always”). The scores of PIUQ–SF–6 ranged from 6 to 30, with higher values indicating increased PIU. The scale also assesses three components of PIU, namely, obsession, neglect, and control disorder, with each calculated based on two items of the six items. The obsession component represents the cognitive engagement with using the internet and the mental symptoms caused by the lack of use. The neglect component explains the extent to which an individual ignores their basic daily needs and activities due to their internet use. The control disorder component measures how difficult for a person to control their internet usage. The scale reliability was evaluated, and Cronbach's alpha value was 0.77 [PIU was measured via the Problematic Internet Use Questionnaire Short-Form (PIUQ–SF–6), which is a self-reporting scale proposed by Demetrovics et al. . The scawas 0.77 .2.2.3The monitoring application employed in this study monitors and stores comprehensive details of the participant's smartphone usage while the phone screen is on, such as screen lock and unlock time, each session of using an application, application name, and session start and end timestamps. The participants' usage data for the first seven days were extracted, and the averages of the time spent on communication apps, social media apps, gaming apps, and other apps were calculated. The usage of a seven-day period was considered in this study as previous research concluded that a minimum of five days is required to reflect weekly usage behavior . We also2.2.4https://psychometriclab.com/obtaining-the-teique/). TEIQue-SF consists of 30 questions that are used to measure the global EI trait score (from 1 to 7) along with four different components constituting emotional self-perception (components scores range from 1 to 7). Emotionality (8 items) measures the ability of an individual to perceive and connect with emotions in one's self and others. Self-control (6 items) measures the ability to regulate emotions, stress, and impulsive behavior. Well-being (6 items) measures personal feelings on achievements and expectations. Sociability component (6 items) measures whether the person perceives themselves as socially confident and effective in communication and participates in social events [EI was measured using Trait Emotional Intelligence Questionnaire short version (TEIQue-SF) , which wl events . The rell events .2.3Millions of usage records were pre-processed using Python 3.8 . The pre33.1Descriptive statistics of the participants are shown in 3.2The test assumptions for ANOVA were met for the living conditions variable. The one-way ANOVA result revealed no statistically significant difference on the PIU whether the participant lived alone, with a partner or family, or with a roommate  = 1.20, and p = .311).3.32 =. 079). A follow-up post hoc analysis showed a significant difference between the Unhappy group  and Somewhat Unhappy group  with p = .012, the Unhappy group and Somewhat Happy group  with p = .005, and the Unhappy group and Happy group  with p = .020. The results indicated that the PIU is higher in the Unhappy group.A one-way ANOVA test was also conducted to assess the differences of PIU across the different levels of users' happiness with time spent using the phone. The test assumptions for ANOVA were checked. Levene's test was not significant , indicating no violation of the homogeneity of variance. Normality was checked with a Q-Q plot, where no deviations were noted. There was a significant difference among the four groups of happiness with the amount of phone usage  = 7.55, p < .001, η2 = .072). The post hoc test showed a significant difference only between the Unhappy group  and Somewhat Unhappy group  with p = <.001. The difference between the other groups was insignificant.Similarly, the test assumptions for one-way ANOVA were checked to assess the differences in PIU across the different levels of participants' happiness with the frequency of phone checking. Levene's test was not significant , indicating no violation of the homogeneity of variance. Normality was checked with a Q-Q plot, where no deviations were noted. There was a significant difference among the groups of happiness with the frequency of phone checking  = 6.85, p < .001, η3.4A multiple regression analysis was conducted to examine the predictive effect of gender, age, amount of smartphone usage in its four types, EI components, happiness with phone usage, and happiness with the frequency of phone checking on PIU. Prior to performing the regression analysis, the assumptions were checked and verified. The dependent variable PIU was visually normally distributed, and in reference to Kim , skewnesR2 = .325, adjusted R2 = .287  = 8.69, p < .001)). The strongest predictor was trait EI self-control , followed by the average of social media usage , and then the average of communication usage  and happiness with phone usage .As shown in 3.5The assumptions of multiple regression were verified. The collinearity statistics confirmed no multicollinearity among variables as VIF values were <5 for all predictors and Tolerance values were >0.2. The Pearson's correlation also showed no multicollinearity among variables, as shown in R2 = .238, adjusted R2 of .196  = 5.64, p < .001)), the variance of neglect by 18.4%  = 4.07, p < .001)), and the variance of control disorder by 25.5%  = 6.18, p < .001)). In the three models, gender and age remained insignificant predictors of each PIU component. The average usage of communication apps, and social media apps, the dichotomous variable “Has_Social_Usage?”, EI's self-control, and happiness with phone usage contributed significantly to the prediction of PIU's obsession. The average usage of gaming apps, EI's self-control, and EI's emotionality were significant predictors of the PIU's neglect. The average usage of social media was close to significantly predicting PIU's neglect. The average usage of communication apps, EI's self-control, EI's sociability, and happiness with phone usage were significant predictors of PIU's control disorder. The strongest predictor of PIU's obsession was EI's self-control  followed by the average social media apps usage . The strongest predictor of PIU's neglect was EI's self-control  followed by EI's emotionality . For PIU's control disorder, the strongest predictor was EI's self-control  followed by happiness with phone usage . In comparing the contribution of variables to the models of each PIU component, the variables were shown to contribute the highest to the variance of PIU's control disorder (25.5%).The multiple regression models, as shown in 4The current study adds to the literature by investigating the impact of smartphone apps usage based on objectively recorded data, EI Components, and subjective happiness with smartphone use factors on PIU and its components while taking participants’ demographic characteristics into account. The study is also one of the first to assess the risk factors for PIU in a diverse sample of participants from various age groups, living conditions, and countries.From the multiple regression, it is apparent that the demographic characteristics of age and gender were not associated with PIU (RQ1). Our findings echo that of Beranuy et al. in , which sFor the demographic characteristic of living conditions (RQ1), the one-way ANOVA analysis revealed that PIU was not significantly different among participants in different living conditions, whether they lived alone or with others. It could be argued that people living alone are more likely to feel loneliness, which was often found as a predictor for PIU . The eviThe inconsistent findings on the association between gender, age, and living condition with PIU could be a result of the different age ranges of the participants or the scales used for PIU in the different studies. There are other environmental factors that could also impact this association. For example, PIU level was found higher in boys with higher internet access at home . COVID-1The multiple regression analysis showed that only subjective happiness with phone usage, communication usage, social media usage, gaming usage, and EI's self-control were found to be significant predictors of PIU (RQ2). Literature has shown that subjective positive feelings about own life conditions negatively correlate with PIU , 83. HowThe amount of communication usage, social media usage, and gaming usage were positively associated with PIU. The amount of social media usage had a higher impact on PIU than the usage of communication apps and gaming apps, which is consistent with the study in . The incEI and its components on the one hand and PIU on the other [In terms of emotional intelligence components, EI's self-control was negatively associated with PIU. EI could play a protective role against PIU  because he other , 92, 93.he other . Accordihe other , individFor (RQ3), of the three regression models where the outcome were PIU components, the models' predictors contributed higher to the variance of the control disorder component. The regression model results also revealed that being happy with phone usage was a significant and negative predictor of the PIU's obsession and control disorder components. The amount of usage of communication apps was a significant and positive predictor of the PIU's obsession and control disorder components. The amount of usage of social media apps, and having social media apps, represented through the variable “Has_Social_Usage?” were significant and positive predictors of the PIU's component of obsession. The amount of usage of gaming apps was a significant and positive predictor of the PIU's neglect component. Our findings contradict a previous study that revealed no correlation between the amount of social media apps usage and gaming apps usage on the one hand and the PIU's obsession component on the other . The finAdditionally, there is scarce research exploring the association between trait EI components and the components—not only the total score—of PIU. Our study addressed this gap and showed significant and negative associations between trait EI components measured using TEIQue-SF and PIU measured using PIUQ–SF–6. This finding was in line with a previous study that investigated the association between trait EI components measured using TEIQue-SF and PIU using the Problematic Internet Use Questionnaire (PIUQ) scale . The resThe current study provided empirical evidence of the impact of different user's profile and personality factors on PIU and its components. The results can be utilized when developing educational or technological interventions for PIU. Educational programs may incorporate awareness of the overuse of the three types of apps investigated in our study as risk factors of PIU. They may also work on improving the EI skills of an individual as a strategy to prevent PIU, depending on the type of apps being overly used. Such educational programs about PIU risk factors and EI skills can be delivered through the same medium, e.g., technology, in a personalized way. According to research, personalized learning is an effective and motivating method for engaging learners and improving learning outcomes , 102. PeThe current study is subject to several limitations. Due to the cross-sectional design, our findings do not necessarily imply causal relationships among variables. Further longitudinal studies are needed to establish causal relationships. Although time spent on the internet and time spent on smartphones overlap , a study5PIU prediction allows for an early intervention to prevent it. PIU prediction models can utilize different personal metrics, including personality and emotional intelligence traits, and also behavioral metrics such as amount, frequency, and type of technology usage, and other life activities such as exercises. Our study investigated the impact of the different app types on PIU. This is one of the few to incorporate objective data about smartphone usage collected through a designated app to investigate the impact of the amount of usage on PIU. Data about the amount of usage is more likely to generate more reliable results when collected objectively. According to the findings, people with high amounts of communication apps usage, social media apps usage, and gaming apps usage are prone to have higher PIU. The amount of usage of other apps had no effect on PIU. The results provided empirical evidence to support the literature that the type of internet usage, rather than the overall time spent on the internet, is a better indicator of PIU . Future Sameha Alshakhsi, Raian Ali: conceived and designed the experiments; contributed reagents, materials, analysis tools or data; performed the experiments; analyzed and interpreted the data; wrote the paper.Khansa Chemnad, John McAlaney: performed the experiments; materials, analysis tools or data.Mohamed Basel Almourad: conceived and designed the experiments; contributed reagents, materials, analysis tools or data.Majid Altuwairiqi: contributed reagents, materials, analysis tools or data.Zayed University: R180053, Taif University: 1-441-79. Open Access funding was provided by the Qatar National Library.This work was supported by Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.


Understanding the effect of stress, fatigue, and sleep deprivation on the ability to maintain an alert and attentive state in an ecologically valid setting is of importance as lapsing attention can, in many safety-critical professions, have devastating consequences. Here we studied the effect of close-quarters battle (CQ battle) exercise combined with overnight military training with sleep deprivation on cognitive performance, namely sustained attention and response inhibition. In addition, the effect of the CQ battle and overnight training on cardiac activity [heart rate and root mean square of the successive differences (RMSSD)] during the cognitive testing and the relationship between cardiac activity and cognitive performance were examined. Cognitive performance was measured with the psychomotor vigilance task (PVT) and the sustained attention to response task (SART). Altogether 45 conscripts participated in the study. The conscripts were divided into control (CON) and experimental (EXP) groups. The CON completed the training day after a night of sleep and the EXP after the overnight military training with no sleep. Results showed that the effect of the overnight training on cognitive performance and the between-group difference in heart rate (HR) and heart rate variability (HRV) depended on the cognitive test. Surprisingly, the cognitive performance was not largely affected by the CQ battle. However, as expected, the CQ battle resulted in a significant decrease in RMSSD and an increase in HR measured during the cognitive testing. Similarly, the HR parameters were related to cognitive performance, but the relationship was found only with the PVT. In conclusion, fatigue due to the overnight training impaired the ability to maintain sufficient alertness level. However, this impairment in arousal upregulation was counteracted by the arousing nature of the SART. Hence, the conscripts' cognitive performance was mainly preserved when performing a stimulating task, despite the fatigue from the sleep loss of the preceding night and physical activity. In safety-critical occupations, such as rescue and recovery, police, and military, maintaining a high level of performance in challenging or even adverse conditions is critical Krueger, . As the via increased activation of the PNS, while the SNS activation is inhibited. The Yerkes-Dodson law (1908) states that a sufficient vigilance or energetic level is needed to retain an appropriate level of focus on any given task. However, excessive or minimal arousal often negatively affects the performance  and psychometric vigilance task (PVT) are commonly used to study psychomotor processing, particularly, sustained attention, vigilance, response inhibition, and arousal  is closely related to brain activity. According to the neurovisceral integration model  or without (CON) sleep loss on arousal, vigilance, and decision-making in a field setting. The cognitive performance was measured using PVT and SART as they capture mental abilities relevant to many occasions in safety-critical fields. Situations can rapidly switch from passive surveillance to a fast tempo-operative activity. The former may require the detection of subtle environmental changes and arousal upregulation while the latter demands quick decision-making. The PVT can be considered a monitoring task, indicating arousal and attentional state  that the performance of those participants who completed the overnight training (EXP) would be impaired due to decreased arousal and vigilance and reduced self-regulative capacity, compared with their well-rested counterparts (CON) during the first SART and PVT test . This would become evident during the SART as in prolonged RT and increased percentage of error of commissions (EoC) and during the PVT in prolonged RT and an increased number of attention lapses. Second, the CQ battle was hypothesized (H2) to result in high acute stress during the cognitive testing, impairing the performance of both groups in the SART with the mentioned performance measures . Again, EXP was expected to perform worse than CON during the SART tests after the CQ battle (midTest and postTest). On the other hand, during the PVT (postTest), EXP performance would be protected by battle-induced stress and arousal. Therefore, EXP performance would reach the level of CON measured with PVT RT, and attention lapses after the CQ battle. In addition, it was hypothesized (H3) that the CQ battle-related high acute stress would become evident in HR and RMSSD  during the SART (midTest and postTest vs. preTest) and PVT (postTest vs. preTest). Furthermore, EXP participants' RMSSD was expected to be lower and HR higher than that of CON participants during each SART and PVT tests. Lastly, higher PNS activity was hypothesized (H4) to be associated with enhanced executive functioning, e.g., increased flexibility in responding. On the other hand, low PNS activity was expected to reflect greater alertness and wakefulness . Therefore, it was hypothesized that higher RMSSD and lower HR would be related to better performance, as indicated by shorter mean RT and decreased EoC during the SART. During the PVT, increased HR and lowered RMSSD was expected to be associated with a shorter median RT and fewer attention lapses.Mage = 20.10, SD = 0.96)] from the Finnish Defense Forces gave their informed consent and took part in baseline measurements. Ten participants withdrew from the study (due to illness or suspected illness) before the military training phase. Thus, the final data set consisted of 45 participants who all completed the entire study. The age of these participants varied from 19 to 24 years .Fifty-five conscripts . There was 38.03 ms  slowing in mean RT during postTest compared with the preTest. The mean RT of the midTest did not differ from the preTest RT  was the final model. A significant effect of the testing session on mean RT was observed in the simpler model . The more there were EoC, the shorter was the mean RT . In addition, a significant mean RT and group interaction was present . The slope of EXP between mean RT and EoC was significantly steeper than that of CON. .The mean RT had a highly significant effect on EoC ) in comparison to EXP (Mdn = 300.0 ms [IQR = 56.8]). The model fit improved significantly after adding group in the model, χ2 (1) = 11.10, p < 0.01. However, adding the interaction term did not further improve the model fit. Group was a significant predictor in the model . EXP was 47.04 ms  slower than CON during both testing sessions . EXP had significantly more (Mdn = 8.5 [IQR = 14.8]) attentional lapses than the CON (Mdn = 1.0 [IQR = 2.0]). The model fit improved significantly after adding group in the model, χ2 (1) = 9.33, p < 0.01. However, adding the interaction term did not further improve the model fit. Group was a significant predictor in the model . EXP had 8.10  attentional lapses more than CON during both testing sessions  during preTest than EXP (Mdn = 87.5 bpm [IQR = 10.2]). Adding group into the model did not improve the model fit. The model with testing session as the only fixed effect was the final model. The testing session had a highly significant effect on the mean HR . Mean HR was 9.26 bpm higher during the midTest  and 3.36 bpm during the postTest  compared with the preTest  decrease in HR from the midTest to the postTest.CON had a lower HR  during the preTest than the EXP (Mdn = 38.0 ms [IQR = 28.6]). The model fit improved significantly after adding group in the model χ2 (1) = 5.39, p < 0.05. However, adding testing session and group interaction did not further improve the model fit. The testing session had a highly significant effect on RMSSD . Similarly, the group had a significant effect on RMSSD . The RMSSD decreased 27.31 ms  during the midTest and 14.33 ms  during the postTest compared to the preTest RMSSD. Furthermore, EXP had 19.98 ms  lower RMSSD than CON over the three testing sessions  increase in RMSSD from the midTest to the postTest.CON had a higher RMSSD  = 3.80, p < 0.1] when accounting for the variability of the group in the model. In the low HR group, there was marginally more EoC  in comparison to the high HR group. The categorized HR variable was not related to the mean RT.The categorized HR was marginally related to the EoC  in the model after accounting for the effect of group . The group with low HR was 19.69 ms slower compared with the high HR group   . HoweverF = 4.03, p < 0.05] when including group in the model, which was also a significant predictor in the model . In the high RMSSD category, the median RT was 18.68 ms longer  than in the low RMSSD category  = 7.66, p < 0.01], group , testing session , and RMSSD categorical variable and testing session interaction  were significant predictors. Pairwise comparisons using Tukey's HSD test showed that CON high RMSSD group was 64.70 ms (SD = 16.5) faster than EXP high RMSSD group (t(54.3) = −3.93, p < 0.01), and EXP low RMSSD was 45 ms (SD = 11.8) faster than the EXP high RMSSD during both testing sessions. In addition, the EXP low RMSSD group RT did not differ from the RT of CON low or high RMSSD groups.Similarly, the categorized RMSSD variable explained a significant amount of variability in PVT median RT [category . HoweverThe current study investigated the effect of the overnight military field exercise with sleep loss and high acute stress caused by military battle exercises on attentive abilities, response inhibition, and arousal. The main findings of the present study showed that the overnight training led to an impairment in upregulating arousal and attention. In contrast, sustained attention and response inhibition seemed to remain intact in a more stimulating and engaging task. Furthermore, the CQ battle led to a significant increase in stress in both groups, which in the end, negatively affected the attentional ability.When tested before the CQ battle, the EXP participants' arousal and attentional state were impaired compared with the CON participants as expected (H1). Longer RTs and increased attentional lapses indicated slower psychomotor processing speed and increased attentional disengagement during the PVT. The finding is in line with the previous studies showing reduced activation in the frontoparietal sustained attention network and subcortical arousal and motor systems in conjunction with slow responses during continuous performance tasks under sleep loss , this happened at the expense of the response speed; the participants' psychomotor slowing became evident, but only after the CQ battle was over. This is consistent with research showing that fatigue reduces cortical activity after urban combat involving uncontrollability and unexpectedness , the SNS activation of the EXP group, as indicated by HR, and PNS withdrawal, as indicated by RMSSD, increased compared to the CON group which had a regular night of sleep. In addition, the CQ battle led to a significant increase in the SNS activation and PNS withdrawal during the second and third SART tests with both groups as hypothesized (H3). Furthermore, the SNS activation of all the participants decreased, and PNS activation increased from the second to the third test, indicating a partial recovery in the autonomic balance. As expected (H3), the EXP participants' PNS withdrawal was larger than the well-rested participants also during the second and third SART tests. However, no between-group differences in sympathetic activation were found during the second and third tests. Interestingly, RMSSD was the only index that differentiated the groups in each test. The recovery in the ANS balance was, on the one hand, likely due to the fact that the third SART test was conducted later after the CQ battle than the second, and on the other, possibly due to the higher intensity of the first track (Track A) compared with the second Track B; . The curSurprisingly, contrary to the hypothesis (H3), no overnight training or CQ battle effect on the ANS activity was detected during the PVT. During the last PVT, after completing both CQ battle tracks, the ANS activity of both groups regressed to the pre-CQ battle level. It should be highlighted that the PVT was the final test administered 30 min after the CQ battle was over. This was the most likely reason for the shift from the sympathetic dominance, which was evident during the SART, toward an increased vagally mediated HRV, i.e., lower arousal. In contrast to the findings regarding the ANS activity during the SART, the groups did not differ in the ANS activity during the PVT tests, suggesting test-related effects.post-hoc tests indicated that the more alert EXP participants (low RMSSD) had comparable arousal and alertness with the low and high PNS activity CON participants. Only the EXP participants with high PNS activity (high RMSSD) had significantly decreased arousal and alertness compared with the low PNS activity EXP and CON and high PNS activity CON participants. However, the ANS activity was not related to attentional disengagement . In other words, despite comparable arousal between the low PNS activation EXP participants and both low and high PNS activity CON participants, the low PNS activity EXP participants continued to have significantly more fluctuation in their alertness compared with CON participants. Contrary to expected (H4), the ANS activity was unrelated to the SART performance.Finally, we divided the participants into two groups based on the median of the PNS activity (low and high RMSSD) and the median of the SNS activity (low and high HR) to test the last hypothesis (H4). As expected, the participants with below-median PNS activation (low RMSSD) had higher arousal and alertness than those with the above-median PNS activation (high RMSSD) as measured with the PVT . Similarly, the above-median SNS excitation (high HR) participants had higher arousal and alertness than those with below-median excitation (low HR). Hence, the more relaxed participants, evident in HR and HRV, were less aroused and alert, as indicated by the PVT RT. Interestingly, the In the upcoming sections, we will discuss the results found in the study more in-depth. First, we go through the results regarding the differences between the SART and PVT performance in conjunction with the HR parameters. Second, we focus on the expectation that the battle-induced arousal protects the post-overnight training performance.The findings regarding the SART and PVT performance in conjunction with the ANS activity during the tests may relate to differences in groups' attentional effort allocation. According to the attentional control theory, one can overcome a lowered ability to perform by exerting an additional, compensatory effort  activation. In a normal wake state, the locus coeruleus noradrenergic projections to the cerebral cortex maintain a sufficient arousal level  as the slowing was not present after the first track. In addition, the co-occurrence of a high error rate with slow motor responses reflects slow processing speed or lapsing attention  that the stimulating nature of the CQ battle would protect the EXP participants' arousal and attentive state was not fully supported. This was probably due to the fact that ANS activity returned to pre-battle levels during the PVT after the CQ battle. However, we obtained some support for the expectation (H4) that the sleep-deprived individuals would benefit from the CQ battle-induced arousal increase during a simple vigilance task. The findings indicated that the EXP participants with low PNS activity have increased alertness and attentive ability compared to the EXP participants with high PNS activity. However, the effect was observed before and after the CQ battle. Therefore, it is likely that the excitement or tension related to the battle exercise may have led to a parasympathetic withdrawal among some EXP participants. Still, we found a significant decrement in attentional engagement even among these more alert participants. In other words, despite the ability for producing equally fast psychomotor responses, they continued having a large number of attentional lapses. However, the current study cannot disentangle why some EXP participants had low PNS activation and increased wakefulness. The findings from Chua et al.  may prova priori. Second, the first team of EXP started the CQ battle at 7:20 a.m., while the first team of CON started their CQ battle 1 h later, at 8:25 a.m. In line with the starting time, the last EXP team finished the CQ battle training at 3:48 p.m., and the last CON team finished the CQ battle at 5:03 p.m. Consequently, the groups did not perfectly match with respect to their circadian phase; there was a difference of approximately 1 h in the morning and 1 h in the afternoon between the study groups. Altogether five CON teams and five EXP teams were perfectly matched concerning time. As the alertness level is known to be influenced by both the time spent awake and the circadian phase . Furthermore, it is reasonable to assume that some individuals may be more vulnerable to the effects of sleep loss. In addition, it is concluded that such CQ battle can be used to induce high acute stress. Finally, this study found that PNS withdrawal is dependent on and possibly related to effort allocation/motivation and task type. RMSSD is more sensitive than HR in indicating changes in effort/motivation/stress.tomi.passi@ttl.fi, satu.pakarinen@ttl.fi.The participants have not given their consent to publish their data. However, the data can be made available for inspection if requested. Requests to access these datasets should be directed to The studies involving human participants were reviewed and approved by Ethics Committee of the Hospital District of Helsinki and Uusimaa and Finnish Defense Forces. The patients/participants provided their written informed consent to participate in this study.TP: study conception, methodology, formal analysis, investigation, data collection, data curation, and manuscript preparation—writing the initial draft. SP: study conception, methodology, investigation, data collection, writing—review and editing, funding acquisition, supervision, and project administration. KL: data curation, investigation, study conception, writing—review and editing, and data collection. JL: investigation, data collection, supervision, project administration, funding acquisition, writing—review and editing. JN: investigation, data collection, project administration, and writing—review and editing. JR: investigation, data collection, and data curation. JV, KP, KK, and TO: resources and writing—review and editing. SM: writing—review and editing. All authors contributed to the article and approved the submitted version.This study was funded by the Finnish Defence Forces (AQ9520) as a part of a four-year research and development program.Authors JL and JN are employed by VTT Technical Research Centre of Finland Ltd. Author JR is employed by Savox Communications Oy Ab. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationship 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.


Polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs) are widespread across the globe, existing in the environment in complex mixtures potentially capable of initiating respiratory illnesses. Here, we use an in silico approach to evaluate the potential pro-inflammatory effects of various carcinogenic PAHs and VOCs through their binding affinity towards the human toll-like receptor 4 (TLR4). For receptors and ligands, RCSB Protein Data Bank and PubChem were used in obtaining their 3D structures, respectively. Autodock Vina was utilized to obtain the best docking poses and binding affinities of each PAH and VOC. Out of the 14 PAHs included in this study, indenopyrene, benzo(ghi)perylene, and benzo[a]pyrene had the highest binding affinity values of −10, −9, and −8.9 kcal/mol, respectively. For the VOCs, out of the 10 compounds studied, benzene, 1,4-dichlorobenzene, and styrene had the highest binding affinity values of −3.6, −3.9, and −4.6 kcal/mol, respectively. Compounds with higher affinity than LPS  could potentially induce inflammation, while compounds with lower affinity would be less likely to induce an inflammatory response. Meanwhile, molecular dynamics simulation and RMSF statistical analysis proved that the protein, TLR4, stably preserve its conformation despite ligand interactions. Overall, the structure of the TLR4 was considered inflexible. TLR2 and TLR4 are toll-like receptors that are involved in the innate immune system. Both identify main bacterial components, namely lipoproteins in TLR2 and lipopolysaccharides (LPS) in TLR4. TLR2, TLR4, and TLR7 are the best characterized pattern recognition receptors (PRRs), which identify invading pathogens outside the cell as well as intracellular pathogens captured in endosomes ,2,3. TLRPolycyclic Aromatic Hydrocarbons (PAHs) are groups of dietary and environmental contaminants that are highly diverse. PAHs are pervasive environmental contaminants formed mostly by the incomplete combustion of coal, oil, petrol, and wood. They arise through insufficient kindling of organic matter and exist in the environment in the form of complex mixtures, in gaseous form, and in particulate matter (PM), as well as in soil particles and sediments. They also contribute to certain health disorders correlating to immunotoxicity, endocrine disruption, or tumor growth . PAHs arVolatile organic compounds (VOCs) are present in a wide variety of consumer items, including furniture, sealants, and paints. VOCs have been connected to lung disorders due to their high degree of reactivity with the airway epithelium and mucosa. VOCs may cause a variety of health problems when inhaled. Propylene glycol and glycol ethers (PG), benzene, and formaldehyde are just a few of the VOCs that are known to react strongly with the respiratory tract’s epithelial lining and mucous membrane. Additionally, exposure to VOC is related to an increased risk of developing asthma and other respiratory illnesses. Although particulate matter exposure results in oxidative stress, VOC exposure results in a substantial degree of chemical reactivity with the amine-rich epithelial lining and mucosal membrane through a C–O polar bonding mechanism, resulting in inflammation. Thus, VOC mitigation solutions must take the chemical makeup of VOCs into account . In urbaLipopolysaccharide (LPS), a component of the cell wall of gram-negative bacteria, is widely regarded as a significant pathogenic component in bacterial infection and can eventually induce systemic inflammatory response syndrome. In response to infection, the immune system produces proinflammatory mediators such as IL-6 and TNF-α, but excessive production will lead to systemic inflammatory response syndrome. LPS triggers macrophages or monocytes through TLR4. LPS is recognized by TRL4 along with additional molecules like CD14 and MD-2 . LPS is The contact of a chemical molecule with a protein may trigger a biological activity. This encompasses the stimulation or inhibition of an enzyme’s activity in addition to the impact of a pharmaceutical substance on its target protein. Binding affinity measures the force with which a binding molecule, sometimes called a ligand, attaches to its matching protein . The binCarcinogens are chemicals that contribute to the formation of cancer, which may be described as the uncontrollable growth of neoplastic cells. Cancer can be caused by a variety of factors. Any one of the roughly 100 trillion cells that make up a human body has the potential to be changed into a cancerous or malignant cell by a number of different agents. These agents could have a chemical make-up, a biological make-up, or a physical make-up . The ideIn light of the fact that in silico methods, such as molecular docking, are acknowledged to possess faster procedural turnover with a wide range of accessibility to other bioinformatics tools, applying such methods in medicine and healthcare studies has gained popularity. In comparison to conventional approaches to cancer research, the cost and amount of time spent on the investigation of the carcinogenic potential of PAHs and VOCs are practically more competitive. Because it makes use of high-throughput molecular docking, the in silico approach made it possible to choose the best-docked ligands from among a vast array of compounds. In aid of deeper understanding, the findings of this study have assisted in the assessment of the pro-inflammatory potential of PAHs and VOCs via the TLR4 pathway, the information of which can be used as baseline to raise awareness among health enthusiasts, organizations, and regular citizens. Furthermore, this may serve as a reference for researchers exploring similar topics. With sufficient knowledge regarding the pro-inflammatory effects of PAHs and VOCs, the results of the research have provided a better grasp on how to handle and reduce the risks of inevitable exposure to the aforementioned substances and to facilitate confirmatory wet laboratory or in vivo experiments in the future.The focus of this study is the potential pro-inflammatory effects of 14 PAHs and 10 VOCs via TLR4 using an in silico approach. The selected compounds are considered carcinogens or possibly carcinogenic to human health. Autodock Vina v.1.2.0  was used to compute the binding affinity of the carcinogens with the TLR4 receptor.The plausible mechanism of the activation of the TLR4 signaling pathway by various PAHs and VOCs, and LPS is shown in https://sourceforge.net/projects/openbabel/, accessed on 2 June 2022). Using the BIOVIA Discovery Studio Visualizer v.21.1 , water and other heteroatoms were removed from the molecule and the binding site was defined from a single subunit with xyz center  and size coordinates  respectively. The protein was finally prepared for docking through the addition of polar hydrogen and Koolman charges using AutoDockTools (ADT) v.1.5.6  and saved as pdbqt. Additionally, the stability and reliability of the prepared protein were examined using the following; Zlab , and Verify 3D and ERRAT  servers.The human TLR4 served as the receptor. The 3D structure was downloaded as sdf file (PDB ID: 4G8A) and converted to pdb via Open Babel v.2.4.0 , acenaphthene (CID:139069599), acenaphthylene (CID:9161), fluorene (CID:6853), phenanthrene (CID:995), anthracene (CID:8418), fluoranthene (CID:9154), pyrene (CID:31423), benzo[a]anthracene (CID:5954), chrysene (CID:9171), benzo[k]fluoranthene (CID:9158), benzo[a]pyrene (CID:2336), benzo(ghi)perylene (CID:9117), and indenopyrene (CID: 9131), while the 10 VOCs are methylene chloride (CID:6344), vinyl chloride (CID:6338), 1,2-dichloroethane (CID:11), chloroform (CID:6212), trichloroethylene (CID:6575), tetrachloroethylene (CID:31373), 1,3-butadiene (CID:7845), benzene (CID:241), 1,4- dichlorobenzene (CID:4685), and styrene (CID:7501). Additionally, LPS (CID:11970143) was also obtained from PubChem and served as the basis for comparison of the binding affinity. All structures of PAHs, VOCs, and LPS in pdbqt format were compiled in a folder to be used for molecular docking later on.In a new and separate folder, the pdbqt files for the receptor and the ligands were copied along with the AutoDock Vina programs following the recommended procedure. Before actual docking, preparation of the docking configuration file (conf.txt) was created using Windows notepad. The input file in text format contained the receptor (TLR4), the ligand, and the xyz size and center coordinates. The configuration file for each ligand was saved in the same folder.Intermediary tasks, such as the production of pdbqt files for the TLR4 protein and ligands and the building of grid boxes with the use of the graphical user interface ADT were double checked for the TLR4 protein . AutoDock Vina v.1.2.0.  was used to perform docking, utilizing the protein and ligand information from the configuration file, as well as grid box parameters. Exhaustiveness was set at 8. TLR4 was treated as stiff throughout the docking phase. The findings with a positional root-mean-square deviation (RMSD) less than 1.0 were clustered together and represented by the result with the lowest binding free energy. For additional study, the docking pose with the lowest binding energy was extracted and matched with the TLR4 receptor structure  and visuhttp://biocomp.chem.uw.edu.pl/CABSflex2, accessed on 2 June 2022) to validate the stability of the complex and evaluate the conformation changes in the protein. The initial structure files for the apo (unbound) structure and the bound structures of TLR4 were uploaded in pdb formats. The raw data for the fluctuation plot of the apo structure as well as the structure for each TLR4 bound to PAH and VOC were transferred into a MATLAB sheet. The 2D data were plotted using MATLAB R2021a , wherein the RMSF of each amino acid is plotted against the residue number. Meanwhile, for the statistical analysis, paired t-test (Microsoft Excel 2010) was applied wherein the significance value was set at 0.05. With this, the p-values of each chain within the TLR4 are further interpreted; p-values that are lower than 0.05 are an indication of significantly different fluctuations in the apo and the bound structures of TLR4.All molecular dynamics simulations (MDS) were performed using the CABS-flex 2.0 server  and psi (ψ) angles. Based on The compatibility of the TLR4 3D model with its sequence was measured using VERIFY 3D . The VERThe overall quality factor for non-bonded atomic interactions between various atoms within the modeled structure was evaluated using ERRAT. The ERRAT score should at least be 50% to be considered a model of good quality . In FiguOverall, the structure quality assessment made using the online tools. Zlab, VERIFY 3D, and ERRAT, suggested that the prepared human TLR4 is a suitable model for molecular docking simulations. From the Ramachandran plot , 98.018%The binding affinity, or docking score in units of kcal/mol was automatically calculated by Autodock Vina based on the total number of pairs of atoms that may move relative to one another or which can be identified as effects of intermolecular and intramolecular forces of attraction. The more negative the binding affinity, the greater the binding stability .PAHs’ carcinogenicity is linked to the number of benzenoid rings in the molecule. As the number of rings increases, the carcinogenic effect also increases. As listed by the United States Environmental Protection Agency (USEPA), 11 of which are carcinogenic or mutagenic PAHs and are further classified based on carcinogenicity. For its well-known carcinogenic properties, benzo(a)pyrene has frequently been used as a guide for the carcinogenicity of other PAHs, and the majority of researchers have used benzo(a)pyrene. It should be noted that in addition to benzo(a)pyrene, research on other PAHs is also necessary .The human TLR4 is considered as one of the most efficient innate immune receptors in the body which induces a proinflammatory response after it binds to a ligands with known pathogen associated molecular patterns . Table 1Interactions between proteins are orchestrated in a precise and time-dependent manner, underlying cellular function. The binding of two proteins can be viewed as a reversible and rapid process in an equilibrium that is governed by the law of mass action. The binding affinity is the strength of the interaction between two (or more than two) molecules that bind reversibly .Each docking experiment often results in the production of a library of ligands, which includes known inhibitors and known decoys. After a docking run is complete, the ligands are ranked according to their binding affinity to the target site, allowing for comparisons between the ligands. AutoDock Vina displays binding affinity in kcal/mol; the more negative the binding affinity, the better the fit within the binding pocket .Styrene, 1,4-dichlorobenzene, and benzene have the highest binding affinity among the 10 VOCs reported in TLR4/MD-2 complex found on the cell surface of target cells is responsible for the attachment of LPS and activation of NF-κB downstream signaling pathway. The best-docked scoring pose as determined by AutoDock Vina was selected and visualized by using BIOVIA Discovery Studio Visualizer .The stimulation of TLR4 by LPS modulated pathways involves inflammation, permeability, and coagulation. Besides its inflammatory reactions, it can also mediate changes in VOC which have the potential to be used as biological markers . Figure Toll-Like Receptors (TLRs) may play a crucial role in pathogen recognition, innate immune activation, and malignancy in human esophageal epithelial cells. The binding affinity of PAHs to TLR4 was evaluated using AutoDock Vina . FurtherLife forms rely heavily on molecular recognition, which pertains to the ability of biomolecules to recognize one another or other tiny molecules with great specificity and affinity. Proteins, a significant class of biological molecules, carry out their activities via binding to other components or to themselves . Most inFrom Almost all of the static and dynamic features of matter, such as their presence in solid, liquid, and gaseous forms, relative stability, and chemical reactivity, are regulated by intermolecular forces, which are defined by the fundamental balance of interacting components of physical and chemical nature . The intThe Ramachandran plot, which includes the phi and psi angles, is essential for predicting the conformational space of an amino acid in a polypeptide using the hard-sphere model that represents steric coupling effects. Without the participation of the phi and psi angles, steric hindrances significantly constrain the protein’s structure. Observing the conformation of a structure was discovered empirically by plotting it in the phi–psi space. The integrity and validity of a protein’s 3D structure are determined by the conformational phi- and psi-derived spaces using the Ramachandran plot .For benzo[a]pyrene, benzo[ghi]perylene, and indenopyrene, all the amino acid residues are located within the favored region A–C. FurtHuman TLR4 interacts hydrophobically with LPS at 40 amino acid residues . Table 4Of the three PAHs investigated, two amino acid residues were shared between each ligand and LPS. The shared amino acid residues vary from Phe-121, Phe-119, Phe-151, and Val-135. Hydrophobic interactions were found to occur at chain D.MDS are performed in order to confirm the flexibility and dynamics of the modeled protein. The amino acid residues with the highest degree of flexibility were predicted with the help of the CABS-Flex server . This seThe value of the root mean square fluctuations (RMSF) obtained from CABS-Flex are indicative of high residue flexibility . RMSF ist-test verified no significant difference in the fluctuations between both structures. Values obtained from the t-test are as follows (p = 0.8287), chain B (p = 0.6087), chain C (p = 0.0520), and chain D perylene  and C (p = 0.0099) indicate that the apo and bound structure is significantly different as verified by the paired t-test in p = 0.2583) and chain A (p = 0.0546) proved otherwise. Moreover, once the TLR4 is bound to indenopyrene (p = 0.9232), B (p = 0.7445), and D (p = 0.1247). However, fluctuations in chain C are visible (p ≈ 0.00). TLR4 bound to benzo[a]pyrene A–D shows follows : chain Aperylene E–H, the d)pyrene I–L, minid)pyrene  also verTo better understand the binding mechanism, structural behavior, and flexibility of TLR4 protein, MDS was performed . DetaileProtein–ligand interactions happen through sub-atomic mechanics, resulting in the conformational changes in which the ligand-binding affects the protein state and its function. The relatedness of the docked structure was estimated by calculating the root mean square deviation (RMSD)  between The structure and physical features of molecular systems are largely determined by noncovalent interactions . It can It is found that only one amino acid residue interacts with benzene. The creation of molecular complexes via intermolecular interactions is extensively investigated using the computer approach known as molecular docking. Computer-aided drug design has shown the effectiveness of molecular docking in studying molecular recognition by predicting the affinity and binding mode between the interacting molecules, such as protein–ligand interactions .The intermolecular forces of attraction between the human TLR4 and the individual VOCs are mostly van der Waals interactions. The benzene ring A interacWhen determining whether or not a model is legitimate, it is necessary to take a number of factors into consideration, including the interactions between surrounding residues, the atoms that are involved, and the positions of the residues. The stereochemical qualities of the model, including bond angles, bond lengths, correct chirality, and ring structure, along with other geometric factors, need to be validated. These attributes are included in the model. The Ramachandran plot is a plot in which the torsional angles of the amino acids phi and psi in a protein sequence are shown in a two-dimensional format (2D). In order to determine the conformation of the peptide, the values are plotted against one another, and the angular spectrum ranges between 180 and +180 degrees on both the x- and y-axes. The Ramachandran outlier is a depiction of those amino acids that reside in areas of the plot that are not favorable .In the Ramachandran plot of TLR-4 amino acids interacting with benzene, 1,4−dichlorobenzene, and styrene A,C,E, alIt is possible to depict the three-dimensional structure of a protein as a graph, with nodes standing in for residues, and edges denoting the strength of non-covalent interactions. Depending on where the amino acids are located in the basic structure, these protein contact networks may be divided into long-range interaction networks and short-range interaction networks .As seen in To further evaluate the reliability of the docked ligand–protein complexes in 3D coordinates of the best docking configuration, TLR4 with its bound VOCs was subjected to MDS . CABS-FlHigher RMSF values are indicative of the high residue flexibility  while lot-test verified there are no significant differences as the p-values are 0.1761 and 0.8625 for chains A and C, respectively . This is visible in p = 0.6545) and B (p = 0.9934). In chains C and D, fluctuations were noticeable with maximum fluctuation reaching 7 Å with p values of ≈0.00 and 0.0210, respectively. p values.For TLR4 bound to benzene A–D, miniectively . For chaThe concept of binding mechanism, structural behavior, and flexibility of the protein complex can be better understood through molecular dynamics simulations . DetaileThe models 2.5 [From 2.5  and is o2.5 . Benzo of the LPS obtained for the Autodock Vina’s scoring function as a standard to determine the VOCs’ proinflammatory capacity, benzene having the binding affinity of −3.6 kcal/mol and 1,4-dichlorobenzene with a binding affinity of −3.9 kcal/mol, can only be possibly less proinflammatory to humans. However, the case is not the same for styrene which has a binding affinity of −4.6 kcal/mol. As compared to the binding affinity of the LPS, styrene is a possible potent compound in inducing inflammation.According to the United States Environmental Protection Agency , volatilVirtual screening utilizing molecular docking algorithms has grown in popularity as a method for developing novel medications and deciphering molecular mechanisms due to time and financial savings associated with pre-analyses using in silico drug screening compared to direct conventional laboratory research. In this study, we used a computational protein–ligand docking approach and modeled interactions between selected PAHs and VOCs and human TLR4.50, pro-inflammatory induction in macrophages) are required to corroborate these in silico findings.Out of the 14 PAHs analyzed, benzo(a)pyrene, benzo(ghi)perylene, and indenopyrene had the highest binding affinity and therefore can be considered highly carcinogenic due to their potential proinflammatory capacities. Consequently, out of the 10 VOCs studied, benzene, 1,4-dichlorobenzene, and styrene obtained the highest binding affinities for potential induction of the TLR4 inflammatory pathway. More in vitro and in vivo studies (e.g., lipophilicity, hydrophilicity, logP, ICFor the molecular docking simulation, the RMSF graphs and superimposed models of apo-TLR4 and TLR4 complexed with PAHs and VOCs confirmed that the structure of TLR4 was stable. It is possible that in silico studies can be utilized in conjunction with clinical experiments to help guide the creation of more precise hypotheses and experimental research regarding the possible effects of PAHs and VOCs in inducing cancer. Preliminary information can be gathered using molecular docking, molecular dynamics, and in silico experiments, which then can be used to guide research in future in vivo and in vitro studies. In multidisciplinary research, this type of computational tool can help researchers and doctors modify their experimental plans in an effort to reduce expenses and improve research efficiency.In future studies, in vitro studies are necessary to ascertain the potential pro-inflammatory effects of other PAHs and VOCs in relation to the human TLR4 molecule, which could also lead to the determination of its toxicity and carcinogenicity. For the development of the TLR4 signaling modulators, it is recommended to analyze the effects of the PAHs and VOCs based on the production of inflammatory cytokines. Lastly, different carcinogenic pollutants may also be tested to compare their effects against the PAHs and VOCs mentioned in this study.


The aim of the present study was to investigate goal orientations and classroom goal structures and their relationship with strategies of self-regulated learning (SRL) in students with and without learning disabilities (LD) and reading comprehension difficulties (RCD). The sample consisted of 537 students attending the two last grades of primary school, fifth and sixth grade . Of these, 58 students were diagnosed with LD, and 70 students, after individually administered assessments in reading accuracy and reading comprehension, were assigned to the RCD group. Self-reported questionnaires were administered, assessing students’ personal goal orientations, classroom goal structures, and strategies of SRL. The results showed that students with LD and students with RCD scored lower in mastery orientation and higher in performance avoidance compared to their peers without difficulties (ND). LD students reported lower scores of adaptive strategies than their peers. In addition, the results confirmed the adaptive character of mastery-approach goals and mastery goal structures and the negative effects of performance-avoidance goals and performance goal structures on the adaptive strategies of SRL. Performance-approach goals predicted adaptive behaviors for all students, confirming the argument of an adaptive type of motivation. The findings of the current study highlight the importance of goal orientations and classroom goal structures for students’ SRL. Implications of the findings for enhancing motivation for students with LD and students with RCD are discussed. Self-regulated learning (SRL) constitutes an essential skill for learning and psychosocial adjustment of students of all ages ,2. AccorEspecially for students with learning disabilities (LD), for whom motivation is of primary importance for learning involvement and achievement, research on achievement goals is relatively limited and the findings conflicting . Moreovemastery goals, which correspond to the opportunity to learn, acquire knowledge, and develop competency [performance goals, which correspond to competence demonstration and social/peer comparison [Achievement goals are conceptualized as the reason or the purpose for which someone is engaged in an achievement situation . Accordimpetency ,11 and pmparison ,19. Accomparison , by addimparison . Neverthmparison ,23. On tmparison ,24,25,26mparison ,28.classroom goal structures [Given that personal attitudes and students’ motivation toward learning tasks are gradually shaped by the influence received from familiar surroundings, including the school classroom setting and the opposite ,30, goalructures ,24. Clasructures ,31. Althructures , includiructures .Empirical research data have shown that adopting a goal orientation and being in a classroom that promotes a specific goal is associated with either positive or negative outcomes, creating a divided model of adaptive and maladaptive goals ,35. ΜastOn the other hand, findings regarding performance-approach goals are contradictory ,49. ΤherFinally, performance-avoidance goals have been described mainly as maladaptive, as they are positively linked to negative outcomes in both cognitive and emotional domains ,64, inclAs with personal achievement goals, students’ perceptions of mastery classroom goals or goal structures have been associated with positive behavior patterns, such as mastery goals, deep strategies, effort, internal motives, help-seeking, support autonomy, and positive affect ,75,76. MAt-risk students or low achievers often present difficult motivational and behavioral profiles ,83,84. NStruggling readers present a similar motivational profile to LD students reporting low motivation, negative attitudes toward reading, surface approach to learning, poor self-regulation skills, and low use of deep approach as compared to skilled readers ,104,105.Although the effects of performance-approach goals are not sufficiently clear , it is aIn the present study, we examined goal orientations, classroom goal structures, and strategies of SRL in three separate groups: students with LD, students with RCD, and students with no difficulties, with the aim of describing their motivational profiles. A second aim was to explore the different patterns of predictors for adaptive and surface strategies for the three groups, aiming to investigate the outcomes of personal goals and classroom goal structures. In this context, we explored the adaptive or non-adaptive role of performance-approach goals on self-regulation. The literature regarding the effects of this personal goal has been inconclusive, on the one hand supporting the adaptive character of the goal and, on the other, promoting the negative outcomes ,115.In this study, the emphasis is given to students with LD and students with RCD, given the few and inconsistent findings of previous studies that have not led to a clear pattern for students facing difficulties ,116. MosIt is more likely that students with LD and students with RCD would report lower scores of mastery-approach goals and mastery goal structures and higher performance-avoidance goals and performance goal structures than students without difficulties;It is more likely that students with LD and students with RCD would report lower levels of adaptive self-regulating strategies  and higher levels of surface strategies as compared to students with no difficulties;In terms of differences between the LD group and the RCD group, it is less likely that LD students would present an adaptive profile, reporting fewer mastery goals, more performance-avoidance goals, more performance goal structures, and fewer adaptive strategies;Are personal goals and classroom goal structures predictors of SRL strategies, and which of them is a predictor of adaptive and surface strategies separately for the three groups?Taking into account the existing literature, the research hypotheses and research question were formed as below:Five hundred and thirty-seven students attending the 5th and 6th grades of 28 public primary schools in Athens, Greece, participated in the study, selected from a larger pool of 568 students. The mean age of the participating students was 11.28 years . All students were native speakers of Greek. Of them, 58 students had a formal statement of learning disabilities  by the state assessment Centers for Differential Diagnosis, Diagnosis, and Support (KEDASY). To get a formal statement of learning disabilities, the following criteria were met: average intelligence using the WISC-III test    and the deep strategies with 10 items  . Motivational strategies contain 4 items  , monitoring contains 7 items,  , and persistence includes 6 items such as “Even if I would rather do other things, I finish my schoolwork . In accordance with the theoretical models, deep strategies, motivational strategies, monitoring, and persistence were categorized as adaptive self-regulating strategies.Children’s Perceived Use of Self-Regulated Learning Inventory  is a selA word reading task and a non-word reading task of the Greek standardized Reading Test TEST-A  were admTwo short informative texts, from the reading comprehension task of the Reading Test TEST-A  were admThe questionnaires and individual assessments were administered to students during school hours by the researcher. Students completed the self-report questionnaires in the classroom, and then they were assessed individually in reading accuracy and comprehension tasks. Students were assisted with their reading by the researcher, if necessary, especially in the case of students with learning disabilities. The study was approved by the Hellenic Institute of Educational Policy of the Ministry of Education, a body that granted consent for access to schools, and parental consent was a prerequisite for students’ participation in the study. Students having parental consent were also asked if they were willing to participate in the study, and they were informed that they had the opportunity to withdraw from the study at any stage.p < 0.05). Kruskal–Wallis tests were performed to test for differences in median scores on goal orientations and classroom goal structures between the three groups of students  as the normality hypothesis was rejected for all variables . There were statistically significant differences between the medians of the groups for all the goal orientations and classroom goal structures , a series of linear regressions analyses was run to estimate the effect of each one of the predictors  and their possible interactions on the dependent variables. The assumptions of the linear regression analysis were not violated . Starting from “Surface Strategies”, a linear regression model that included all the main effects was conducted. Moving forward, we eliminated all the predictors that did not have a statistically significant effect-each time, the predictor with the highest non-significant p-value was excluded-and then tried to add only significant interaction terms.F = 140.579, p = 0.000, R2 = 0.570) included only statistically significant main effects .The procedure was repeated until the Model 5  = 102.809, p = 0.000 R2 = 0.576) the group RCD by P-STR term had a statistically significant effect on surface strategies (p = 0.005 < α = 0.05). It was observed that, for the models 6 and 7 that contained the other two possible interaction terms, the interaction terms did not have a statistically significant effect on surface strategies (both p > 0.05).p = 0.017 < α = 0.05). Looking into Model 8, it was observed that there was a positive effect of MAP, PAP, and P-STR on surface strategies, and more specifically, if MAP was increased by one unit, then the surface strategies was increased by 0.097 on average. Similarly, if PAP was increased by one unit, the surface strategies was increased by 0.490 ceteris paribus. In the case of the ND group (level of reference of group), when P-STR was increased by one unit, the surface strategies was increased by 0.363 on average, ceteris paribus. Furthermore, we observed that also for the LD group, P-STR had a similar effect on surface strategies with the ND group since the interaction term group LD by P-STR was not statistically significant (p = 0.854 > α = 0.05). On the other hand, for the RCD group, the interaction term group RCD by P-STR was statistically significant (p = 0.005 < α = 0.05) with a positive coefficient of 0.257, which means that the increase rate in the surface strategies was higher than the other two groups (0.36 + 0.26 = 0.62) when the P-STR was increased by one unit. It should be noted that on average, the group RCD had a lower coefficient (−0.411) as an initial value compared to ND (level of reference) and LD (0.518). Bringing together the main group effect and the interaction term, regarding the RCD group, for low values of P-STR, the surface strategies value was lower than the other two groups but as P-STR increased, the surface strategies increase was more aggressive than for the other two groups. To better visualize the interaction effect of P-STR and groups on surface strategies, we utilized the sjPlot R package [The final model was Model 8, as the ANOVA tests comparison showed that Model 8 had a statistically significantly better fit than Model 5  = 199.570, p = 0.000, R2 = 0.751), an ANOVA test was utilized to evaluate if the latest model (Model 2) showed a similar fit to the initial one (Model 1). No statistically significant difference was found in the fit between Model 1 and Model 2 (p = 0.8054 > 0.05), so Model 2 was used as a base to identify significant interactions .As Model 2  consisteF = 162.208, p = 0.000, R2 = 0.755) had a statistically significant effect on adaptive strategies (p = 0.011 < α = 0.05). All the other models did not have any extra significant terms.Running linear regressions models with interactions, we observed that only the Group LD by MAP interaction term in Model 4  . However, for the LD group, there was a negative interaction term MAP by group (−0.266) that decreased the overall positive effect of MAP on the adaptive strategies score (0.493 − 0.266 = 0.227). So, for the LD group, a unit increase in MAP brought a 0.227 increase in adaptive strategies. The above differences in the slopes of MAP for each group level are also presented in the estimated marginal means interaction plot below, where the blue line (LD group) has a smaller slope than the slopes of the other two groups , the estimated marginal means together with their 95% confidence intervals were calculated  and pairp < 0.0001 and p = 0.0021 < 0.05), and marginally, there is no statistically significant difference between LD and RCD surface strategies (p = 0.0641 < α = 0.05). Moreover, based on the estimated marginal means comparison t-test with Tukey adjustment, it was observed that the LD group had a statistically significantly lower adaptive strategies score than the two other groups (p < 0.001 and p = 0.003), while the ND and RCD groups showed similar performance (p = 0.124).From the Tukey tests, we concluded that the mean ND surface strategies were statistically significantly lower than the ones of LD and RCD  by adopting a personal goal or being in an environment that promotes specific goals for self-regulated learning of all students, regardless of the type of difficulty . MasteryHowever, the reverse pattern was observed in performance-avoidance goals. Avoiding learning engagement for fear of low performance and adopting performance-avoidance goals were found to be positively linked to surface strategies and were negatively linked to adaptive strategies. More specifically, performance-avoidance goals negatively predicted adaptive strategies of self-regulated learning in all groups. This result confirms the extant literature that supports the less adaptive character of one’s performance-avoidance goals toward learning behaviors and school performance ,70,96. PAccording to the present study, performance-approach goals significantly predicted surface strategies for all groups, in agreement with the literature . MoreoveTaking the above results into consideration, emphasis is put on the value of classroom goals and personal mastery goals for learning, but also on the negative effect of personal performance-avoidance goals for all students, regardless of their presented difficulties. Promoting mastery of classroom goals and therefore encouraging students to adopt personal goals of acquiring knowledge and improving themselves by individual standards is of the essence in the classroom . At the The use of self-report may lead students to report strategies that they have not used, offering socially desirable responses, or that they might be unaware of strategies they used automatically. It is equally probable that the individual characteristics of students are incorporated into the perceived classroom goals, thus highlighting the subjective nature of the interpretation of educational practices within the classroom. For example, the relationship between teachers and students or between peers could be an important factor affecting students’ attitudes toward the school environment; however, this was not taken into account in the present study . Future These findings of the present study expand our knowledge of achievement goals and self-regulated learning strategies in students with comprehension difficulties and students with learning disabilities. Given the importance of motivation in the learning process, the results of the present study may have implications for the identification of the motivational profile of at-risk students and the implementation of primary and secondary prevention programs with the aim of forming self-regulated learners.