This questionnaire elicited students’ self-reported changes to health-related behaviours, mental well-being and academic engagement across 4 defined time-points: . Many items were adapted from previous Irish research and additional validated scales included the Alcohol Use Disorders Identification Test (AUDIT-C) and the World Health Organisation’s Well-being scale (WHO-5). Key findings revealed that at T1, substantially more males reported ‘good/very good’ general health than females (76.3% vs. 70.8%), while physical activity patterns followed a similar trend at both T0 (80% vs. 66.1%) and T1 (66.7% vs. 61%). A total of 78.4% of participants reported a body mass gain from T0 to T3, thus reflecting the reduced physical activity levels and compromised nutritional patterns across this period. Worryingly, AUDIT-C scale data revealed hazardous drinking habits were evident in both males and females, while fruit and vegetable intake, physical activity levels, and mental well-being among this cohort remained notably sub-optimal. Ratings of positive academic engagement also decreased substantially between T0 (90.3%) and T3 (30.4%). These findings substantiate the rationale for tailored health promotion interventions in university settings to support students’ transition back to traditional programme delivery and, of equal importance, to improve general health and well-being post-COVID-19 within this cohort.Given the well-established impact of COVID-19 on university students’ health and lifestyle parameters, the current study sought to investigate these impacts within an Irish university setting. A cross-sectional design was employed, with a 68-item questionnaire instrument disseminated to all Year 2 undergraduate students in the host institution (N = 2752), yielding a 9.7% response rate ( Only fully completed responses were stored on the Qualtrics platform. Spoiled, implausible, outlier, and/or missing data points were reviewed and discussed by two members of the research team were deemed appropriate. To ensure consistency in analysis, all self-reported height and body mass data were mathematically converted to metric equivalents where required. BMI was calculated by the following formula [p < 0.05 for all tests.Numerical data were summarised using means, standard deviations, and median/interquartile ranges, while all categorical data were summarised using relative frequencies. Health-related behaviours, well-being parameters, and academic engagement were (i) dichotomised by self-reported academic grade, prior to the COVID-19 pandemic (T0) (ii) analysed by gender. These were categorised as higher-grade category—‘60% or more’, or lower-grade category—‘59% or less’, based on the Irish grading system classifying 60% or more as a first- or second-class honours. Between-gender differences were explored using chi-squared tests for independence  and/or Mann–Whitney U tests for non-parametric numerical data as appropriate. Between-group gender analyses were primarily conducted between groups who identified as ‘male’ and ‘female’. Where relevant, descriptive analyses were also carried out for participants who selected ‘other’ or ‘prefer not to say’, although relative size of this group was low (n = 5). Paired sample t-tests were conducted to compare fruit and vegetable consumption across timepoints T0–T3. The alpha level of significance was set at Binary logistic regression was used to identify significant predictors of participants’ self-reported body mass gain from the onset of COVID-19 to time of data collection. A dependant outcome dichotomised variable was derived as; 1 = increase in body mass, 0 = all other responses (including (i) no change in body habitus, (ii) that they had lost body mass, and/or (iii) didn’t know), based on two relevant questionnaire items  “Do you feel you have? Gained weight/Lost weight?”). The independent variables selected, based on results from preliminary exploratory tests of association (chi-square tests), were gender, age group , academic grade range (dichotomised), calculated BMI category, feelings about body mass, likert-type dietary statements, changes to sleep during lockdown, self-perceived BMI category, adherence to physical activity guidelines during lockdown (students’ semester one of 2nd year), habitual fruit and vegetable intake during lockdown (students’ semester one of 2nd year), perceptions of impact upon physical activity levels, and classified drinking status based on AUDIT-C ranking. For the logistic regression analysis, between the dependant variables of interest that were coded for the dichotomous outcome variable , there was no evidence of multi-collinearity amongst the selected independent variables. Data were collected from a total of 268 participants, representing a response rate of 9.7%. The mean completion time of the questionnaire was 16.7 ± 5.3 min. Data from two participants were subsequently removed as they did not meet the inclusion criteria , therefore data from 266 participants that fully completed the questionnaire were analysed. n = 76), 69.5% as female (n = 185), and 1.9% identified as ‘other’ or ‘preferred not to say’ (n = 5), with an age range from 18–56 years . Of the total respondents, 28.6% identified as male (n = 193) rated their overall general health as either ‘good’  or ‘very good’ , while gender was not significantly associated with general health rating . When participants were dichotomised by their self-reported academic performance, 71.4% of the higher-grade category were in this positive general health category (good’ or ‘very good’.At T3 (time of data collection), the majority of participants . Self-reported PA and sedentary levels displayed an inverse relationship from T0 through to T2 (see Prior to the onset of the COVID-19 pandemic (T0), a greater proportion of males  reported that they were meeting the Irish PA guidelines  in comparison to their female counterparts . At each timepoint thereafter, females continued to report lower levels of PA than males, however the gap between genders reduced during T2 and T3, with males reported PA levels dropping by 17.4% between T1 and T2 . No significant associations were found between gender and sedentary levels  of participants perceived a change in their body mass from pre-COVID-19 (T0) to time of data collection (T3), with 78.4% (n = 152) reporting a gain in body mass. Participants’ mean self-reported body mass gain was 7.6 kg (range:1–30 kg). A relatively lower proportion  reported losing body mass, with a mean body mass loss of 8.4 kg (range: 1.36–26.8 kg).Participants were asked how they felt about their current body mass  compared to pre-COVID-19, with 35% reporting that they felt either ‘poor’ (24.1%), or ‘very poor’ (10.9%) in this regard. ‘Calculated’ versus ‘self-perceived’ BMI categories were examined based on those participants who reported both parameters correctly (n = 232), with participants who only reported one data point such as body mass, removed , revealiA regression model was utilised to investigate predictors of body mass gain. The model identified 61.3% (Nagelkerke R square) of the variance in predicted body mass gain and correctly classified 82.6% of cases. The percentage of cases that were correctly predicted by the model  was 86.7%. The specificity of the model that correctly predicted participants that reported either (i) no change in body habitus, (ii) that they had lost body mass, and/or (iii) didn’t know, was 76.7%. The positive predictive value, the percentage of correctly predicted cases with the observed characteristic compared to the total number of cases predicted as having gained body mass was 84.3%. The negative predictive value, which is the percentage of correctly predicted cases without the observed characteristic compared to the total number of cases predicted as not having gained body mass, was 80.0%.The model identified four significant independent variables of the predictors of body mass gain; namely those (i) aged > 24 years, (ii) feeling ‘poor’ or ‘very poor’ about body mass, (iii) perceiving their BMI as overweight/obese, and (iv) reporting more sleep. There was no evidence of multi-collinearity from either (i) bivariate correlations between independent variables, or (ii) collinearity diagnostics  between 1.1–1.9). An inspection of standardised residual values revealed that there were ten outliers, , which were kept in the dataset. Participants were less likely to have gained body mass if they ‘agree,’ ‘neither agree or disagree’ or ‘disagree/strongly disagree’ compared to those who ‘strongly agreed’ with the dietary statement ‘I sometimes eat because I am bored’. Interestingly, those who reported ‘poor’, or ‘very poor’ sleep quality were less likely to gain body mass . p = 0.12). Those who reported feeling ‘poor/very poor’ about their body mass were 4.9 times more likely to report body mass gain than those who reported ‘good/very good’ (p = 0.00). In relation to participants who perceived themselves to be overweight/obese, they were 5.71 times more likely to report body mass gain versus those who perceived themselves as normal weight/underweight (p = 0.03). Sleep quality and sleep duration were both predictors of body mass gain, with those who reported more sleep presenting as 6.4 times more likely to report body mass gain (p = 0.01), and those who reported ‘poor/very poor’ sleep quality presenting as less likely to have reported body mass gain in comparison to those whose sleep was ‘good/very good’ . Similarly, those who answered, ‘strongly agree’ to the dietary statement ‘I sometimes eat because I am bored’, were those more likely to have gained body mass versus those who answered ‘agree’ (OR = 0.22) to ‘disagree/strongly disagree’ .The data also revealed that gender was not an independent variable that predicted the likelihood of body mass gain. The highest predictor of likelihood of body mass gain related to participants aged >24 years, who were 8.1 times more likely to have reported body mass gain in comparison to those in the 18–20 and 21–23 age categories (p > 0.05) via a paired sample t-test. However, there was a significant difference between the mean consumption of daily fruit and vegetable servings from T0 (pre-COVID) to time of data collection T3 (April–June 2021) . A greater proportion of participants who increased their overall fruit and vegetable consumption ‘strongly agreed’ (12.6%) or ‘agreed’ (24.4%) that they ‘were more aware of their calorie consumption’ at T3 . Furthermore, there was a significant association between those who ‘strongly agreed’ (13.5%) or ‘agreed’ (23.0%) that they were ‘more likely to have set mealtimes’ and those who reported an increase in fruit and vegetables at T3 .Daily fruit and vegetable servings consumption increased from 3.9 mean daily servings at T0, to 4.0 mean daily servings at T1, to 4.2 mean daily servings T2; but this increase was not reported as significant (n = 221) of participants reported drinking alcohol habitually, comprising 80.3% of males, (n = 61), 84.3% females, (n = 29), and 80.0% other, (n = 4). The AUDIT-C scale exhibited similar reliability to the previous work of Bickerdike et al. (2019), with a Cronbach’s Alpha of 0.62. Worryingly, based on the AUDIT-C threshold scores, 63.9% (n = 39) of male and 42.2% (n = 65) of female drinkers were categorised as ‘hazardous’ drinkers [p = 0.004). Regarding the initial impact of the stay-at-home period (T1), 66.5% (n = 147) of participants reported changes to their drinking habits with increases in either ‘frequency’, or ‘frequency and volume’, accounting for 41.5% (n = 61) of this cohort. Interestingly, 52.4% (n = 77) reported a decrease in such drinking habits. , 82.3% . However, there was a statistically significant difference between gender and attaining eight hours of sleep per night at the weekend  with 8.6% of males vs. 22.9% of females reporting they obtained this recommended duration. In total, 41.7% (n = 111) of participants reported that they got more hours of sleep during the pandemic  relative to the period prior to its arrival. Contrary to participants’ reported sleep quantity during COVID-19, results revealed that sleep quality scores decreased during these timepoints with 35% reporting ‘poor’ or ‘very poor’ sleep quality during T3, compared to 16.6% reporting these ratings during T0. In terms of self-reported habitual sleep duration during T3, 25.9% of participants reported eight hours of sleep per night on weekdays, which increased to 32.3% on weekends. There was no significant association between gender and sleep duration on weeknights (χn = 92) of participants rated their mental well-being at T3 as ‘good’, 28.6% (n = 76) as ‘neither good nor poor’, 19.2% (n = 51) as ‘poor’, and 10.2% (n = 27) as ‘very poor’. Worryingly, only 7.5% (n = 20) rated their mental well-being as ‘very good’. Notably, 38% (n = 101) reported receiving some form of treatment or support for their mental well-being, either at the time of data collection , or at some time in their life . Of those participants that reported receiving such treatment or support, 18% reported that this was via a friend/family member, 13.9% stated that they had previously spoken to a lecturer/member of staff, 11.7% reported that they had attended their GP, 18% stated that they previously attended a counsellor, 12.4% reported attending a psychiatrist, while 10.2% stated that they had previously received in-patient psychiatric care. Gender did not discriminate between self-rated mental well-being .Findings revealed that 34.6% (p < 0.006) between the three groups, with post hoc tests confirming significant differences between the males vs. females . A Mann–Whitney U test was conducted to determine the gender-differences in WHO-5 scores, revealing statistically significant differences between males and females, U = 5295, z = −3.028, p = 0.002. The options of ‘all of the time’ or ‘most of the time’ were the most frequent answers by participants in response to the statements within this well-being index . Regarding the WHO-5 well-being index   ‘using online learning management’, (3) ‘communication with classmates’, (4) ‘accessing supports’, and (5) ‘attending online lectures/webinars’. A sub-set of specific aspects that constitute the University experience were rated on a 5-point Likert scale, including (1) ‘When reporting on their experience of university across all four timepoints (T0–T3), there was an overall decline in participants feeling ‘very good’ or ‘good’ . Inversen = 266) prior to, and during, the global COVID-19 pandemic. Considering the devasting impact of COVID-19 on population health and wellbeing worldwide, and despite living through this difficult period, the majority of students, 72.6%, rated their general health ‘good’ or ‘very good’ at the time of data collection (T3). Despite the maintenance of students’ general health, these figures are substantially below age group-related norms reported in similar Irish research, where 93% of both males and females aged 15–24 reported their general health as ‘good’ or ‘very good’ [very poor’, despite living and studying through the COVID-19 pandemic. The overall self-reported optimistic general health rating was not seen globally, and contrasts with the findings in Japan by Tahara et al. [The current study serves to provide an insight into the health-related behaviours, well-being, and academic engagement of a cohort of Irish university students  in hazardous drinking habits displayed by males during the COVID-19 pandemic. López-Valenciano et al. [Several studies have reported a stronger correlation between males engaging in weekly physical activity levels than their female counterparts ,31,32,33n = 232), it was revealed that both genders underestimated their BMI category. Regarding males, 22.4% perceived themselves as ‘overweight’ with 31.4% calculating within this category, and 1.8% perceiving their BMI as ‘obese’ with a calculated result of 10.7%. Inversely, more females perceived themselves as overweight  and similarly to males, they under-reported the obese category, with only 2.6% perceiving themselves as being in this BMI category versus the 12.3% calculated scores. It is apparent that university student cohorts have a lack of understanding of the classification of BMI category as similar findings were reported by Bickerdike et al. [In terms of body habitus, 53.6% of participants classified themselves within the ‘normal weight’ BMI category, with 28.7% and 11.8% classified as ‘overweight’ and ‘obese’, respectively. From the cohort that elected to report their self-perceived BMI category, whilst also reporting their height and body mass  include educating students on intentional healthful dietary habits to encourage the targeting of the recommended dietary intakes of macro- and micro-nutrients and (ii) focus on increasing daily physical activity levels to combat the increase in excess body mass gain to combat the negative behaviours incurred during COVID-19. A substantial proportion of participants (72.9%) reported a change in body mass from the onset of COVID-19 (T1) to time of data collection (T3), with five times as many participants indicating increases in body mass versus a loss in body mass. In a similar study conducted by Palmer et al. , data wep = 0.014) from pre-COVID-19 (September-December 2019—3.9 servings) to the time of data collection (April–June 2021—4.3 servings), with the median intake of four servings per day across the timepoints. This self-reported fruit and vegetable intake is higher than the figures reported by Bickerdike et al. [n = 2267) of university students in which the median fruit and vegetable intake was found to be three servings per day. In contrast to these findings, a nationwide survey-based study conducted in China during the COVID-19 pandemic that examined changes in dietary patterns among a youth cohort aged 15–28 years identified a significant decrease in the frequency of fruit and vegetable intake [Achieving the recommended daily intake of fruit and vegetable servings per day (five-seven servings) has many health benefits . In somee et al.  among a e intake . With ree intake ,15 highlDespite the stay-at-home measures that were in place due to COVID-19, high levels of hazardous drinking habits were reported by both males and females in the current study, with 63.9% of males consuming ‘six or more drinks’ and 42.2% of females consuming ‘five or more drinks’ on a typical night out despite the lack of social settings  at this time. Relating back to the work of Bickerdike et al.  in the sn = 1951) about their alcohol consumption during April–May 2020 (T1 in the current study) reporting a reduction in their alcohol consumption substantially by more than 12 units per week since the onset of COVID-19 [Regarding general alcohol consumption habits since the onset of the COVID-19 pandemic, 66.5% of participants in the current study reported changes in alcohol consumption patterns. Other similar studies conducted during COVID-19 reported that 96.5% of university students changed their drinking habits, and, similar to the current study, there was not a significance between groups that decreased or increased their drinking habits . PositivCOVID-19 . As alcoCOVID-19 , and is COVID-19 ,39, thervery poor’ during T0, with this figure increasing to 12.4% at T3, representing a concerning decrease in sleep quality over the COVID-19-related timepoints. Similarly, 50.8% of participants rated their sleep quality as ‘good’ during T0, however, this decreased to 33.1% by T3, outlining a major reduction in participants’ sleep quality. This may be because of the added pressure endured by students as end of semester university exams were ongoing, during the time of data collection. Further analysis across the 4 timepoints revealed a decrease (4.2%) in participants who rated their sleep quality as ‘very good’ between T0 and T1. Interestingly, however, 41.7% (n = 111) of participants reported attaining ‘more sleep’ per night during the lockdown periods, thereby suggesting sleep volume does not equate to sleep quality, within this data set. Martínez-de-Quel et al. [n = 693) both before and during the COVID-19 pandemic and found that the confinement due to the COVID-19 pandemic resulted in a significant decline in the quality of university students’ sleep, thus substantiating the findings observed in the current study. In the current study, 3.8% of participants rated their sleep quality as ‘l et al.  investiggood’ before the onset of the COVID-19 pandemic (T0), while this decreased to 33.1% at the time in which they were completing the questionnaire (T3). This finding may come as a direct result of the added stress to students at the time of data collection due to university exams. These findings suggest the requirement for a university-based intervention aimed at improving students sleep habits. A plethora of research in recent years has demonstrated the negative effects of sub-optimal sleep patterns on academic performance, such as poor brain function and reduced memory functioning ,44,45,46n = 27) of participants rated their mental well-being as ‘very poor’, with only 7.5% (n = 20) rating their mental well-being as ‘very good’. An extensive research study by Ammar et al. [n = 1047), due to the COVID-19 pandemic. The current study also unearthed stark findings related to student mental well-being and related use of support services, with 10.2% of those who previously admitted to receiving treatment or support for their mental well-being stating that they had previously received inpatient psychiatric care. To date, there is a dearth of research examining university student cohorts who have sought in-patient care for their mental well-being. Pedrelli et al. [Within the current study, an array of findings relating to students’ ratings of their mental well-being was reported over the four timepoints. During T3, 10.2% . Similar findings were observed in a study conducted in the Netherlands (n = 166 university students), where it was reported that participants found their online educational experience less satisfactory than traditional in-person lectures [Evidence of positive academic engagement in university students at the onset of the COVID-19 pandemic (T1) was also identified. However, the initial positive response evidentially diminished over time, as the number of participants who rated their experiences of online learning as ‘lectures . To dateThere are several practical implications of the current study. The use of validated scales and derivation from other Irish studies facilitated relevant comparisons of data across a range of thematic areas ,26 partiAlthough the current study appeared to be the first of its kind in an Irish context, there are some limitations that should be noted. This was a cross-sectional study; therefore, findings are limited to relevance and context of the time of data collection and preclude any inferences in terms of causality. That being said, this study has produced a critical overview of the health and well-being of a cohort of university students and provides a valuable insight into the impact of COVID-19 on students’ progress through university across this period. Inevitably, the use of self-reported data may have led to under- or over-reporting in some cases. Future studies may benefit from conducting anthropometric measures to determine greater accuracy in this aspect of the research design. The relatively small sample size in the current study may also be considered a limitation, in addition to the fact that there was a larger response rate from females than males.poor/very poor’ about body mass, perceiving BMI as overweight/obese, strongly agreeing with the dietary statement ‘I sometimes eat because I am bored’, getting more sleep during stay-at-home period, and having very poor sleep quality. These should be considered by policy makers with regard to how healthy lifestyle habits may be facilitated amongst university student cohorts to combat obesity. Suboptimal trends in fruit and vegetable intake were revealed across the 4 pandemic timepoints and need continuous improvement. Throughout COVID-19, hazardous drinking rates were still worryingly high amongst participants and therefore efforts to reduce these levels should be a focus for future health and well-being initiatives. Participants who were classified in the higher risk drinking category were also more likely to have changed their drinking habits during the stay-at-home period with changes in their frequency, volume, or both. 41.7% (n = 111) of participants reported ‘more sleep’ during the lockdown periods, sleep quality reported to be rather low. Therefore, it is imperative for the university setting to focus on encouraging good sleep hygiene. Whilst of significant concern is that fact that results also outlined stark findings in relation to the gender differences in mental well-being, with males obtaining higher scores of positive well-being in the WHO-5 scale than those of females . Of the seven questions that enquired about the four timepoints within this study, academic engagement throughout the pandemic provoked the greatest drop-in satisfaction rate. Interestingly, nearly 11%  of participants reported spending more than seven hours on their phone on an average weekday, which could be a contributing factor to the aforementioned decrease in students’ academic engagement or indeed the increase in poor student mental well-being. Overall, these data serve as an essential resource in contributing to the development of university-based initiatives to support the health and well-being of contemporary student cohorts.As the COVID-19 pandemic is ever-evolving and uncertain, this is a time-critical area for research, with several related studies already conducted in other countries, yet none in an Irish university setting. These studies have identified the major implications experienced by the university student population as a direct effect of the COVID-19 pandemic ,9,11,13.


In the published article, there was an error. The name of the drug “risperidone” was mistakenly written as “aripiprazole” in the Abstract and Results sections.A correction has been made to the Abstract section, Results sub-section, Paragraph 1. This sentence previously stated: “Over a 15-year time horizon, lurasidone yielded an improvement of 0.197 QALYs with a cost saving of CN¥12,093  vs. olanzapine and an improvement of 0.116 QALYs with a cost saving of CN¥6,781 (US$983) vs. aripiprazole.” The corrected sentence appears below: “Over a 15-year time horizon, lurasidone yielded an improvement of 0.197 QALYs with a cost saving of CN¥12,093  vs. olanzapine and an improvement of 0.116 QALYs with a cost saving of CN¥6,781 (US$983) vs. risperidone.”Three corrections have been made to the Results section, Base-case analysis sub-section, Paragraph 1. One sentence previously stated: “Compared with olanzapine and aripiprazole, lurasidone was the dominant strategy associated with reduced costs and increased QALYs.” The corrected sentence appears below: “Compared with olanzapine and risperidone, lurasidone was the dominant strategy associated with reduced costs and increased QALYs.” Another sentence previously stated: “Over a 15-year time horizon, the total cost of patients treated with lurasidone was CN¥1,28,662  and CN¥12,093  lower than that of patients treated with olanzapine, and CN¥6,781 (US$983) lower than that of patients treated with aripiprazole.” The corrected sentence appears below: “Over a 15-year time horizon, the total cost of patients treated with lurasidone was CN¥128,662 , CN¥12,093  lower than that of patients treated with olanzapine, and CN¥6,781 (US$983) lower than that of patients treated with risperidone.” The other sentence previously stated: “Total QALYs of patients treated with lurasidone were 8.147, 0.197 higher than those of patients treated with olanzapine, and 0.116 higher than those of patients treated with aripiprazole.” The corrected sentence appears below: “Total QALYs of patients treated with lurasidone were 8.147, 0.197 higher than those of patients treated with olanzapine, and 0.116 higher than those of patients treated with risperidone.”Two corrections have been made to the Results section, Sensitivity analyses sub-section, Paragraph 1. One sentence previously stated: “Similar results were observed when assessing the cost-effectiveness of lurasidone compared with aripiprazole.” The corrected sentence appears below: “Similar results were observed when assessing the cost-effectiveness of lurasidone compared with risperidone.” The other sentence previously stated: “The results of OWSA comparing lurasidone with olanzapine and lurasidone with aripiprazole are shown in Figure 2, with the top 10 influential parameters presented in the tornado diagram.” The corrected sentence appears below: “The results of OWSA comparing lurasidone with olanzapine and lurasidone with risperidone are shown in Figure 2, with the top 10 influential parameters presented in the tornado diagram.”A correction has been made to the Results section, Sensitivity analyses sub-section, Paragraph 2. This sentence previously stated: “The PSA of 5,000 simulations also showed lurasidone to be cost-effective compared with either olanzapine or aripiprazole at all willingness-to-pay thresholds.” The corrected sentence appears below: “The PSA of 5,000 simulations also showed lurasidone to be cost-effective compared with either olanzapine or risperidone at all willingness-to-pay thresholds.”The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.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.


While QuantiFERON-TB gold (QFT) is frequently used, little attention is paid to the mitogen response. How it could be impacted and associated with outcomes is poorly known.Retrospective, case-control study in hospitalized patients who underwent QFT testing in two hospitals between 2016 and 2019. We defined two groups of cases with either negative  or weak (0.5–2 IU/ml) mitogen response, and one group of controls with normal (>2 IU/ml) mitogen response.p < 0.001] and increasing hospital mortality .A total of 872 patients were included. An ongoing infection was independently associated with both a negative  and a weak mitogen response . Among tuberculosis patients, a weak mitogen response was associated with a false-negative QFT result (75%) compared to a normal response (20%). Decreasing mitogen response  was associated with increasing length of hospital stay [median (interquartile range) 5 (3–13), 11 (5–21) and 15 (10–30) days; Clinicians should take notice of the mitogen response since IFN-γ concentrations lower than <2 IU/ml were associated with false-negative QFT results in tuberculosis patients, independently associated with ongoing infections, and could be associated with worse prognosis. Mycobacterium tuberculosis-specific CD4 + /CD8 + T cell immunity, are recommended for screening LTI  . Gamma ining LTI . The usening LTI . A “neganing LTI –5. Severning LTI –10.While most of the identified factors are related to chronic conditions, little attention has been paid to the factors potentially associated with acute conditions at the time of QFT sampling. For instance, the reported frequency of indeterminate results is higher in septic patients up to 29% ] than in%  than iIn addition, the clinical relevance of the standard 0.5 IU/mL threshold is a matter of debate, and it has been suggested that a response to PHA with IFN-γ < 10 IU/mL could be considered “possibly altered” rather than “normal” .In order to provide a better understanding of indeterminate QFT, our aim was to assess the factors associated with mitogen response, including ongoing or recent infection. The secondary objectives were to study differences between patients with a weak or a negative mitogen response, the association between mitogen response and clinical outcomes, and the QFT results in TB patients according to mitogen response.We conducted a retrospective, observational, case-control study in a cohort of patients hospitalized for at least 48 h in two healthcare facilities [Dijon University hospital and the Georges François Leclerc cancer (CGFL) center] between October 1, 2016 and October 31, 2019, and who underwent QFT testing during hospitalization. Patients under the age of 18, patients only admitted for consultation (as outpatients), those hospitalized for less than 48 h, and those with incomplete QFT results were not included . If a paThe patients who met these criteria were classified into three groups according to the mitogen result of the QFT (IFN-γ concentrations). Cases were defined as patients with a “negative” mitogen response, (IFN-γ ≤ 0.5 IU/ml), as defined by the commercial supplier, or with a “weak” mitogen response (IFN-γ = 0.5–2 IU/ml). Controls were defined as patients with a “normal” mitogen response IFN-γ > 2 IU/ml. For each case (either with “negative” or “weak” mitogen response), we included the control patient who had a QFT performed chronologically just after the corresponding case patient . The mitQuantiFERON-TB gold tests were performed by the same laboratory  according to manufacturer instructions. Briefly, four blood collection tubes were drawn: TB1, TB2, and controls (nil and mitogen). One milliliter of whole blood was incubated for 16–24 h at 37°C, after which plasma was harvested and tested using an enzyme-linked immunosorbent assay for the presence of IFN-γ produced in response to the peptide antigens or PHA (mitogen). The nil result was used to adjust for background, heterophile antibody effects or non-specific IFN-γ in blood samples.Data were collected from the 3 groups of patients constituted, namely the cases  and the control patients .Mycobacterium tuberculosis identification with culture and/or polymerase chain reaction). The presence of an active infection within the last 3 months or at the time of the QFT sampling were recorded if the infection was clinically/biologically suspected or confirmed by the clinician in charge of the patient. Sepsis was considered as severe if requiring ICU admission. Septic shock as sepsis with persisting hypotension requiring vasopressors to maintain mean arterial pressure ≥65 mm Hg and had a serum lactate level >2 mmol/L (18 mg/dL) despite adequate volume resuscitation , or if they had active cancer or hematologic disorder, HIV infection, or primary immune deficiency. Tuberculosis (TB) was recorded if there were associated clinical-radiological signs of TB and it was confirmed with laboratory testing  and are in accordance with French (Loi Informatique et Liberté n°78–17 du 6 janvier 1978) and European (GRPD EU 2016/679) regulations on data protection and patient information (Commitment of compliance MR004 n°2210228 of 3 December 2018) with a waiver of informed consent given the non-interventional study design.Study protocol and data collection were registered with the CNIL , as appropriate. A False Discovery Rate (FDR) was applied to account for multiple comparisons, and the results are expressed in Q-values.Continuous variables were expressed as means ± standard deviation (SD) or medians and inter-quartile range (IQR), according to distribution, and categorical variables were expressed as frequencies and percentages. Comparisons were made between the three groups using Kruskal–Wallis tests or ANOVA when appropriate for continuous variables, and the Chi-square test for qualitative variables. When p-value < 0.2 and at least 15 observations in each group were accounted for. Log-linearity was tested for each continuous variable using fractional polynomials  and GraphPad Prism software version 9.1.1 .To account for potential confounders, polytomous logistic regressions were fitted considering the normal mitogen response group as the reference. First, a model including only clinical data was fitted, followed by a model including both clinical and biological data. Correlations between variables were checked before their inclusion in the model to avoid colinearity. All variables associated with IFN-γ production with a ynomials . A backwDuring the study period, 8,260 QFT were performed, including 2,459 in adult patients hospitalized for at least 48 h . Among tp = 0.06, and 50% female vs. 48%; p = 0.54). Finally, 872 patients were included in the study  with a “normal” mitogen response (IFN-γ > 2 IU/ml), 436 were included as controls. Age and sex in the 436 included control patients did not differ significantly from the 1,587 patients who were not selected as controls  than “normal” response group and less frequently immunocompromised (p < 0.001 for both). The “negative” and “weak” mitogen response groups were not significantly different for these two variables.The clinical and biological characteristics of the three groups are compared in As compared to the “normal” mitogen response group, the “negative” response group were significantly more likely to have had surgery and an infection, or a severe infection within the 3 months preceding the QFT, and those with a “weak” response were significantly more likely to have had a severe infection in the 3 months preceding the QFT.At the time of the QFT sampling, an ongoing infection was detected in, respectively, 18, 33, and 48% of patients with a “normal,” “weak” and a “negative” mitogen response, respectively, and septic shock was detected in 1, 4, and 11%, respectively. Acute infections observed at the time of QFT sampling were mainly bacterial (85%) and of pulmonary origin (56%) .The “normal,” “weak” and “negative” mitogen response (respectively) was associated with decreasing hemoglobin, albumin and lymphocyte counts, but increasing neutrophil count and C-reactive protein levels . In addiWhen considering only clinical variables, multivariable analysis revealed that an ongoing infection at the time of QFT was independently associated with both a “negative” , and a “weak” mitogen response , as well as older age and immunodepression. In addition, male sex, diabetes, and an infection within 3 months before QFT were independently associated with a “negative” mitogen response, but not with a “weak” mitogen response, as compared with a “normal” response Model 1, . In caseWhen the “severe infection at the time of the QFT” variable was added in the model instead of the “an ongoing infection at the time of QFT” variable (model 2), the first variable was independently associated with both a “negative”  and a “weak”  mitogen response .When considering both clinical and biological variables (model 3), multivariable analysis revealed that lower serum albumin, hemoglobin and lymphocytes, but higher neutrophil counts were independently associated with both a “negative” and “weak” mitogen responses . In addition, sex, immunodepression and higher platelets were independently associated with a “negative” mitogen response, and diabetes was associated with a “weak” mitogen response when compared to the “normal” mitogen response group .When the variable “a severe infection at the time of the QFT” was added in the model instead of “an ongoing infection at the time of QFT” (model 4), this first was independently associated with a “negative” mitogen response  .Among the 872 patients included, 18 patients (2%) had microbiologically confirmed TB, among whom 6 (33%) had a false negative QFT . Interep < 0.001), ICU admissions , hospital length of stay  and in-hospital mortality   was associated with an increasing proportion of patient with onset of infectious complications  .Our case-control study involving 872 hospitalized patients who underwent QFT testing yielded four main results. First, the presence of an ongoing infection at the time of QFT was independently associated with a “negative” mitogen response and therefore indeterminate QFT results. Second, patients with a “weak” mitogen response and “negative” mitogen response had several similarities. Third, among confirmed TB patients, a “weak” mitogen response was associated with false negative QFT results. Fourth, the mitogen response was associated with hospital length of stay and in-hospital mortality.In our study, 8% of hospitalized patients had indeterminate QFT results, which is in the range of other previously published studies . MultivaWe then addressed the problem of the cut-offs for mitogen positivity. We observed that patients with “negative” and “weak” mitogen responses have similar characteristics, especially for immunodepression status, which was identified in, respectively, 58 and 52% of these patients, compared with 36% of patients with “normal” response. Our results raise the question of whether a LTI can be confidently interpreted with a “weak” mitogen response. Seeing that such a result is not a “normal” response to PHA stimulation, it may be thought to be a false negative QFT when diagnosing LTI, or even TB. Furthermore, our data for patients with microbiologically confirmed TB are illustrative. QFT was less frequently positive if mitogen response was “weak” (25% of cases) compared to “normal” (80% of cases). In addition, in the patient who underwent serial QFT, there was a correction of both mitogen and TB antigen responses between TB diagnosis and 5 weeks later under treatment.These results offer new insight about QFT performance, particularly during TB, which is associated with both systemic inflammation and T-cell exhaustion. One could assume that decrease in both lymphocyte activation and systemic inflammation (reflected by CRP levels) would improve lymphocyte function. The exhaustion of T lymphocytes is a well-known consequence of sepsis  and otheFinally, in hospitalized patients who underwent a QFT, we observed that weaker IFN-γ production was associated with a worse prognosis, considering infectious complications, ICU admission, in-hospital mortality, and hospital length of stay. Huang et al. previously reported that a lower mitogen response was associated with worse 1-year survival in TB patients . HoweverOur study has several limitations. First, the retrospective design led to missing data, mainly for laboratory results. In addition, this was a single center study when considering the laboratory which performed QFT, and the results may not be reproducible in other settings. However, the laboratory is accredited for QFT analysis (COFRAC accreditation ISO15189 n°1–8125). Third, the cutoff we used to define “weak” mitogen response is rather arbitrary, and whether higher thresholds would be associated with different parameters and outcomes was not assessed.In conclusion, it appears to be of utmost interest to consider the mitogen response in hospitalized patients who undergo QFT, in particular when this response is “negative” or “weak” (IFN-γ ≤ 2 IU/mL). In suspected TB, aside from the response to the TB antigen, it suggests the need for further testing to avoid premature interpretation and misdiagnosis. Outside the scope of TB, the “weak” mitogen response was independently associated with ongoing infections and could be associated with a worse prognosis. The IFN-γ response to mitogen and the threshold of 2 IU/mL may thus serve as a prognostic biomarker in hospitalized patients, even though further confirmation in other cohorts is needed.The original contributions presented in the study are included in the article/Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.MJ, MB, CB, and LP: concept and design. MJ, MB, and A-LS-L: recruitment of patients. MJ, MB, A-KS, CB, and LP: acquisition, analysis, or interpretation of data. MJ, MB, and CB: drafting of the manuscript. MJ, CB, CM, SA, A-LS-L, AC, A-KS, LP, and MB: critical revision. CB and MB: supervision. All authors contributed to the article and approved the submitted version.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.


Despite advances in its treatment, patients diagnosed with clear cell renal cell carcinoma (ccRCC) have a poor prognosis. The mechanism of cuproptosis has been found to differ from other mechanisms that regulate cell death, including apoptosis, iron poisoning, pyrophosphate poisoning, and necrosis. Cuproptosis is an essential component in the regulation of a wide variety of biological processes, such as cell wall remodeling and oxidative stress responses. However, cuproptosis-related genes’ expression in ccRCC patients and their association with the patient’s prognosis remain ambiguous. Evaluation of The Cancer Genome Atlas (TCGA) identified 11 genes associated with cuproptosis that were differently expressed in ccRCC and nearby nontumor tissue. To construct a multigene prognostic model, the prognostic value of 11 genes was assessed and quantified. A signature was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and this signature was used to separate ccRCC patients into different risk clusters, with low-risk patients having a much better prognosis. This five-gene signature, when combined with patients’ clinical characteristics, might serve as one independent predictor of overall survival (OS) in ccRCC patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that cuproptosis-related genes were enriched in patients with ccRCC. Then, quantitative real-time PCR (qPCR) was employed to verify these genes’ expression. Generally, research has indicated that cuproptosis-related genes are important in tumor immunity and can predict OS of ccRCC patients. Renal cell carcinoma (RCC) is the most common type of kidney cancer, accounting for 2.2% of the global cancer burden and 1.8% of tumor-related deaths . CcRCC iThe process of cuproptosis, which is thought to have some key roles in the treatment of malignancies, is presented in the early stages of development . Copper The mechanism that cuproptosis induces cell death has been shown to differ from other mechanisms that control cell death, including apoptosis, iron poisoning, pyrophosphate poisoning, and necrosis . The rolRNA-seq data for ccRCC were acquired from TCGA , includip < 0.05 was chosen as a threshold to maintain the predictive performance of this signature. Further research was conducted on overlapped prognostic DEGs. Interacting networks were established using Search Tool for the Retrieval of Interacting Genes  between tumor and para-carcinoma tissues in TCGA were determined by “limma” package, with cutoffs that included p and false discovery rate (FDR) < 0.05. Relationships between the expression of 13 cuproptosis-related genes and patient survival in TCGA were determined by univariate Cox analysis. sion 11.5. Target sion 11.5, and a pData on patient age, gender, race, tumor stage, survival time, and survival status were obtained for 611 samples in TCGA and 91 samples in ICGC-RECA-EU. The association between these factors and expression of cuproptosis-related genes was estimated by Cox regression analyses, with outcomes visualized by forest maps. Multivariate Cox analysis was also employed by “rsm” package , with thData on the expression of each cuproptosis-related gene in normal and ccRCC tissue samples, as assessed by immunohistochemistry, were obtained from Human Protein Atlas , and emphttps://software.broadinstitute.org/cancer/software/genepattern/). All overlapping genes were subjected to univariate Cox regression analysis to find prognostic genes with p < 0.05. The correlations of these genes with immune cells and pathways were assessed using the “corr.test” function in the “psych” package (https://cran.r-project.org/package=psych).Differential analyses comparing groups with differing levels of risk were assessed by “limma” package, with criteria for difference being |log2FC| ≥ 1 and FDR <0.05. Immune scores in patients with ccRCC were obtained by Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression (ESTIMATE) , with gr2.CcRCC cell line 786-O and human proximal tubular epithelial cell line HK-2 were cultured in Dulbecco’s modified Eagles’ medium  supplemented with 10% fetal bovine serum . Cultivation conditions were maintained with 37°C and 5% COATP7B, 5′-TCTGTGCTGATTGGAAACCGTGAG-3′ (forward) and 5′-CACCGTCAATAGCCACCAGGATG-3′ (reverse); DBT, 5′-TTCTCTGAACCCACCTCTGTCCTG-3’ (forward) and 5′-ACCTGGTCTGTCTGACTGTCTGAG-3’ (reverse); DLAT, 5′-TTGATGTCAGTGTTGCGGTCAGTAC-3’ (forward) and 5′-GTGGCTGTAGTTTACCCTCTCTTGC-3’ (reverse); PDHB, 5′-CGAGGGCTGTGGAAGAAATATGGAG-3’ (forward) and 5′-GGCTTGCATGGAGAAATTGAAGGTC-3’ (reverse); LIAS, 5′-AATAACAGAGGTGGTGCCAGAATGC-3’ (forward) and 5′-GTGCTGGGATTACAGGCGTGAG-3’ (reverse). The levels of mRNA expression were estimated using the 2−ΔCT method after being normalized to β-actin expression levels.Total RNA was extracted from 786-O and HK-2 cell lines, and reverse-transcribed into cDNA. The expression of five genes in our signature was quantified with qPCR. Primers of genes were as follows: The ability of this model to predict OS was evaluated in TCGA and ICGC clinical cohorts. OS of patients in subgroups was evaluated by K-M curves and compared by log-rank tests. This independent prognostic significance of this signature was evaluated by Cox regression analyses. Statistical analyses were employed by R software (version 4.41).ATP7B, DLAT, DLD, LIAS, and SLC31A1 were upregulated and the levels of expression of DBT, FDX1, and PDHB downregulated in tumor samples  + (−0.216* DBT exp.) + (−0.080* DLAT exp.) + (0.056* LIAS exp.) + (0.032* PDHB exp.). A median score was utilized to separate 526 patients into different risk groups of 263 patients each, with PCA showing that these patients were clearly separated into two distinct subsets (p < 0.001)  . ROC ana< 0.001) .p-values for the ATP7B, DBT, DLAT, LIAS, and PDHB genes of 0.164, 0.291, 0.138, 0.177, and 0.868, respectively, (p = 0.010)  cohort. Multivariable Cox analysis of these five genes yielded ctively, . Median ctively, . As in tctively, . K-M ana= 0.010) . Moreove= 0.010) .p = 0.005, hazard ratio (HR) = 1.806, 95% confidence interval (CI): 1.191–2.743] and ICGC   and ICGC   . Multiva9–6.159) . A predi9–6.159) . The red9–6.159) .ATP7B, DLAT, and LIAS proteins were higher, whereas the levels of expression of DBT and PDHB were lower, in tumor samples than in non-tumorous kidney tissue, in accordance with the results of differential mRNA analysis (DBT (p = 0.004), DLAT (p = 0.003), and PDHB (p = 0.004) and OS in these patients  were significantly associated with ccRCC patients. A heatmap shows infiltration scores of 16 immune cells and activation of 13 immune pathways . CD8+ T p < 0.05 identified 25 prognostic DEGs that were associated with cuproptosis and immunity. Analyses of correlations between these genes and immune cells and pathways involved in immune system function found that IGKV1D-43, IGLV4-60, IGLV2-14, IGKV3-15, and IGHV3-30 were significantly associated with immunological status  cells was higher, in the high-risk group . Evaluatl status .With univariate Cox regression analyses, our study evaluated the predictive significance of cuproptosis-related genes based on the differential expression in tumors and adjacent nontumorous tissues of patients in TCGA. Univariate and lasso Cox regression analyses identified eight DEGs that were associated with prognosis in ccRCC patients. This led to the development of a five-gene risk signature for patients with ccRCC, with functional enrichment and immune correlation analyses indicating that this signature was strongly related to immune cells and immune system pathways. Expression levels of five signature genes were validated in vitor.ATP7B, DBT, DLAT, LIAS, and PDHB. ATP7B is a hepato-specific Golgi-localized ATPase involved in the regulation of copper homeostasis as well as in signaling processes (ATP7B in tumor cells (DBT is a subunit of transacylase (E2)  —a major er cells . MoreoveCA cycle . LIAS exxidation . PDHB isid cycle . PDHB exrognosis . DespiteCuproptosis is a newly described mechanism that mediates cell death and has been associated with mitochondrial metabolism . CuproptATP7B, DBT, DLAT, LIAS, and PDHB, differed significantly in ccRCC and adjacent nontumorous kidney tissue and that a gene signature involving these five genes may be independently prognostic of OS in patients with ccRCC. Although the potential function of this gene signature was also assessed by immune activity and functional enrichment analyses, further research is needed to determine the precise mechanism of how these genes influence the incidence, development, treatment, and prognosis of ccRCC. Cuproptosis is closely associated with ccRCC, as many cuproptosis-related genes are differentially expressed in ccRCC and surrounding nontumorous tissues. Moreover, the present study found risk score based on this signature was closely related to the prognosis of ccRCC patients. Analysis of TCGA and ICGC showed that it could be an independent prognostic factor for predicting OS of ccRCC patients. Immunity is also tightly associated with these genes.Little is currently known about the mechanisms of action of cuproptosis-related genes in ccRCC. The present study found that levels of expression of five cuproptosis-related genes, This study had several limitations. For example, publicly available data, such as RNA-seq and clinical data, may have certain drawbacks when analyzing the prognostic performance of gene signatures. Additional data, including primary data from patients with ccRCC, are needed to confirm the predictive significance of this gene signature. Moreover, additional research is required to determine the processes and activities by which cuproptosis-related genes promote tumor development.This study identified a unique signature that can predict the prognosis of ccRCC patients, as well as five cuproptosis-related genes that may be associated with the mechanism of development of ccRCC. Moreover, these results provide a solid foundation for future research into the relationship between cuproptosis and immunity in ccRCC.Multiple bioinformatics methods were used to identify a cuproptosis-related signature that was associated with the prognosis of patients with ccRCC. This signature was found to be associated with incidence, survival, and immunity of ccRCC, suggesting that this signature may be an independent prognostic of OS in patients with ccRCC.


We then numerically evaluate the performance of these different methods for estimating the simplex regression model. The numerical evidence favors inference based on the bootstrap method, in special the bootstrapt interval, which was decisive in an application to real data.In this paper we evaluate the performance of point and interval estimators based on the maximum likelihood(ML) method for the nonlinear simplex regression model. Inferences based on traditional maximum likelihood estimation have good asymptotic properties, but their performance in small samples may not be satisfactory. At out set we consider the maximum likelihood estimation for the parameters of the nonlinear simplex regression model, and so we introduced a bootstrap-based correction for such estimators of this model. We also develop the percentile and bootstrap Currently, several proposals of regression models for doubly-constrained regression models for doubly-constrained response variables, which assume continuous values in  or ), the normal linear model is inadequate, because besides the possibility of occurrence of fitted values smaller than 0(a) or larger than 1(b), in general, the data present asymmetry and heteroscedasticity, violating the usual assumptions of such model. Thus, it seems more appropriate to consider models based on distributions naturally supported on  as is the case of the simplex regression model proposed by  = 2(σ2)2 =0 ; E[(y-μ)= 2(σ2)2  and E[d′]=0 . The Fisthogonal  so that asymptotically unbiased. Thus, as the sample size increases, approximately unbiased and its bias should be close to zero. In theory this fact is true only when n approaches infinity, that is, asymptotically. In practice the better the approximation in ⊤ is a random sample such that each yt, t = 1, …, n, follows a distribution F supposedly known and indexed by parameter vector θ;1: Suppose that θ;2: From the original sample, obtain the B bootstrap samples of size n, namely b = 1, …, B;3: Generate 4: For each bootstrap sample B of times, thus obtaining 5: Repeat steps 3 and 4 a great number b = 1, …, B for compute the desired quantities, for instance: mean, variance, confidence interval, etc. regarding distribution of y.6: Use the estimates θ. Thus, we can obtain an estimator corrected up to second order by bootstrap  be the largest integer less than or equal to the number (B + 1)α/2; then, we define the lower and upper bounds of the percentile interval by the p-th and (B + 1 − p)-th ordered elements of the B bootstrap replicas of  4.1.1: Assuming 0 < t confidence interval, here so-called as ‘Boott’  the largest integer less than or equal to the number (B + 1)α/2. Thus, the quantiles bootstrap k-th and (B + 1 − k)-th ordered elements of Tb*. Therefore, the bootstrapt intervals for the parameters of the simplex nonlinear regression model are given by the following expressionsi = 1, …, k and j = 1, …, q, the i-th and j-th components of the vectors β and γ.Assuming 0 < t intervals outperform the asymptotic interval displaying empirical coverages closer to the exact nominal levels, but tend not to be accurate in actual practice. Percentile intervals are more accurate, but display less satisfactory coverage performances. An outstanding discussion on bootstrap-based confidence intervals can be found in [⋅%]}. For λ ≈ 12, the sets of the ML-Ia, Boott and Bootp estimators are equal to {(0.919), [3.61%][4.49%]}, {(0.950), [2.40%][2.61%]} and {(0.923), [3.20%][4.47%]}. For λ ≈ 45 those sets have become {(0.919), [2.65%][3.21%]}, {(0.950), [2.55%][2.67%]} and {(0.922), [2.97%][3.41]}. Finally, when λ ≈ 128 the respective sets are {(0.917), [3.94%][4.40%]}, {(0.949), [2.66%][2.39%]} and {(0.916), [3.98%][4.48%]}.Our spotlight hereafter is show how the Bootβ2 (μt ≈ 0.5 and μt ≈ 1), when n = 40, λ ≈ 128 and β2 equal to −1.8 and −1.5, respectively. We note that for the three nominal levels and the different scenarios, the asymptotic type confidence interval has the shortest average length. We also note that the bootstrapt confidence interval presents the best empirical coverage and balance proprieties, followed by the Bootp interval which had very similar values to the ML-Ia interval.β2 and γ2, respectively, for n = 40, λ ≈ 128 and the different scenarios for μt, t = 1, …, n. The distinct lines represent the different confidence intervals under evaluation, and their lengths correspond to the respective average lengths. The values below and above of the vertical lines are the non-coverage rates, meaning the percentages of replicates in which the true value of the parameter was smaller than the lower limit of the interval (below) and larger than the upper limit of the range (above).Figs μt scenarios, the analyzed intervals are approximately symmetrical around the true value of β2. We further note that for μt ∈ , the intervals were better balanced when compared to the scenarios where μt ∈  and μt ∈ . Overall, the bootstrapt confidence interval stands out as better balanced. γ2.These graphics were designed according to . Throught confidence interval is slightly asymmetric around γ2. Regarding the bootstrap percentile confidence interval it exhibits very strong asymmetry, especially for the nominal 99% level. We also observe that the asymptotic confidence interval exhibits strong unbalancing, as the rates (% Right) are markedly higher than the observed rates (% Left). However, the bootstrapt and percentile confidence intervals are approximately balanced for all nominal levels and scenarios. Therefore, based on the results presented, we suggest using the bootstrapt confidence interval which showed better coverage and balance performances.The bootstrapY , β2: −1.4166 (−1.5062) [0.0016], β3: −26.1208 (−25.7744) [3.6902], β4: −0.2615 (−0.2571) [0.0635], β5: −0.3366 (−0.3431) [0.0607] and γ1: 0.0455 (0.2782) [0.2673]. Concerning Model-II it is possible to notice differences between the ML estimates and their bootstrap corrected versions. For example, SE of t and ML-Ia interval estimators regarding to the β2 parameter is especially noteworthy. Let interval estimators be ML-Ia, Boott and Bootp, respectively, the interval estimates are ,  and , for 99%. For 95%, ,  and . Finally, ,  and , for 90% Here, we note that the Bootp displays a poor performance. It should be reminded that β2 is the parameter associated with the nonlinearity of the model, as well as β3. In fact, after dispersion was assumed constant the Boott scheme provided intervals for β3 with considerably shorter lengths compared to those of the ‘Model-I’ model.In what follows we shall appoint this model as ‘Model-II’ and provide some quantities about its parameters, namely: the maximum likelihood estimates, their bootstrap corrected versions: ‘(⋅)’ and the standard errors of the estimates ML: ‘[⋅]’. Thus, g(y) and t interval estimator.One area of research that we have been working on intensively regards model selection criteria for nonlinear models. The posed by  for the posed by  and is dIn this paper we evaluate the point and interval estimation for the parameters indexing the nonlinear simplex regression model  in smallOften MLEs can be biased when the sample size is small or even moderate. Thus, we consider comparing the point MLE performances of the model parameters and their corrected versions through a bootstrap scheme. The results of Monte Carlo simulations showed that, in general, the corrected estimators presented lower biases than the maximum likelihood estimators, evidencing the efficacy of the bootstrap scheme in bias correction. The MLEs of the parameters of the dispersion submodel are strongly biased, and the bootstrap corrected estimator provides a substantial reduction of these bias. Thus reinforcing the importance of using the proposed scheme in the bias correction of estimators of the nonlinear simplex regression model.t estimator intervals. Regarding coverage rate in every simulation’s scenarios the bootstrapt confidence interval outperformed the two others competitors. Furthermore, in almost all experiments it was the best balanced interval.Usually the asymptotic confidence intervals based on MLE’s require large samples in order the coverage rates to be close to the nominal ones. An alternative to constructing adequate confidence intervals on small samples is through the bootstrap method. Thus, we consider three competing interval estimators, namely: the MLE-asymptotic, percentile and bootstrapt is typically larger than that of its competitors. Overall, however, the bootstapt interval proved to be the most appropriate estimator interval for nonlinear simplex regression. Not only from the simulation results, but it was also decisive in the application. In a scenario with only n = 28 observations, it was able to point the misspecification of the dispersion model which yield to a new and best fitted model.As a penalty for providing this outperformance, the bootstrapS1 File(OX)Click here for additional data file.S1 Text(TXT)Click here for additional data file.


Hildenbrand et al. Communications Biology 10.1038/s42003-021-01885-w (2021)1. However, these “cephalopod” fossils are a composite (a chimaera) with apertures of common septate orthothecid hyolith conchs with invaginated thin Coleoloides tubes misinterpreted as the diagnostic siphuncles of cephalopods. Cephalopods may have had an Early Cambrian origin, but their oldest undoubted fossils record an appearance and diversification as macropredators much later in the Late Cambrian and at the dawn of the Great Ordovician Evolutionary Interval.Fossils record the lowest known occurrence of mollusc classes in the Cambrian Evolutionary Radiation . A very early occurrence of possible Early Cambrian cephalopods has been claimed2, or do the oldest confidently identified cephalopod conchs record an origin and early diversification of major clades ca. 30 Ma later in the Late Cambrian5. Hildenbrand et al.’s1 interpretation of elongate calcareous conchs from SE Newfoundland seems to support an Early Cambrian origin. However, sufficient data are available in their report to allow comparison with existing reports on coeval elongate septate orthothecid hyoliths of their study locality and across SE Newfoundland and to re-evaluate of their specimens as a chimaera formed by post-mortem, wave-infill of orthothecid hyolith conch apertures with shell debris, including tiny tubular shells misinterpreted as the diagnostic siphuncles of cephalopods. Uncertainty surrounds the origin of cephalopods in early animal diversification. Did these marine predators appear in the Early Cambrian, as suggested by genomic study1 describe Early Cambrian “cephalopods” from Bacon Cove, SE Newfoundland . The eroded, Ediacaran at Bacon Cove is locally covered by ca. 2.0 m of quartzose sandstone of  basal Member 4. A thin stromatolitic bed on the highest Ediacaran is overlain by a thin red limestone with purported “cephalopods.”Hildenbrand et al. 1, fig. 1a, b. Avalonian Cambrian microsparites show early aggradational recrystallization of lime mud13. Skeletal fragments in these successions were originally mainly aragonite, and are now replaced by calcite spar. Original calcite sclerites occur in a few taxa  and retain original microfabric. Few taxa had phosphatic sclerites , and there is negligible phosphatization by comparison with coeval Siberian and South China facies14.Depositional and diagenetic features of the red limestone are key to understanding its “cephalopods.” The limestone has abundant, elongate conchs (3 cm long) and other calcareous fossils that form a packstone with a fine grained microsparite matrix9 a roughly bimodal (east–west) orientation of the elongate conchs consistent with wave action e.g., ref. 15. Wave-dominated deposition of Avalonian peritidal packstones led to complex depositional histories with burial, exhumation, and reburial of fossil debris that may leave internal cavities  empty to completely filled with lime mud. Post-mortem invagination of fossil fragments occurs in sclerite apertures e.g., refs. 1, fig. 1a;16, fig. 7.2. Early diagenetic calcite spar can partly fill open cavities and lime mud can then fill the lumen by infiltration or following exhumation and reburial of a conoidal fragment e.g., refs. 1, fig. 2. These depositional and early diagenetic events are shown by Hildenbrand et al.1, figs.1, 2a, 4.At Bacon Cove, we recorded1 with those of the orthothedid hyolith “Allatheca” degeeri, which has fewer, more widely spaced septae e.g., ref. 16. A closer comparison is with the conchs with variable cross sections of the orthothecid “Ladatheca” cylindrica, which are abundant in Avalonian peritidal facies14; have closely spaced septae; and smaller apical angle17. These features are comparable to the facies of and features of the conchs in Hildenbrand et al.1.The Bacon Cove conchs have been comparedLadatheca” cylindrica. This species has such hyolith  features as an operculum16, fig.9.2 and a fusiform protoconch  in its syntypes16, fig. 9.8.The evidence is that the Bacon Cove “cephalopods” include elongate conchs of the common “Ladatheca” cylindrica conchs have apical septa. Septa are taxonomically non-diagnostic and occur in different animal groups in sclerites  and conchs 19. Septae reduce the volume of energy-demanding soft tissue19 and do not necessarily show homology with septae used in cephalopod buoyancy control e.g., ref. 17.Bacon Cove “19. The latter is defined by a siphuncle—a skeletal structure that perforates and connects the septae3. Hildebrand et al.1, figs. 2a, 4b illustrate tubular structures interpreted as “submarginal siphuncles” with “connecting rings”. However, there is no evidence these tubes connect and perforate septa—a precondition for interpretation as a siphuncle, with perforate septa unknown in the Bacon Cove material.The defining features in ancestral cephalopods are a conch with an open aperture (living chamber) and posterior phragmoconeLadatheca” cylindrica conch matrix1, fig. 1a, b. Invaginated conchs with sparry calcite fill indicating reworking of diagenetically altered (“pre-fossilized”) conchs occur in an “L.” cylindrica conch aperture1, fig. 1a, b—with the septate specimen on the right comparable to “L.” cylindrica. The tiny conch on the left is likely referable to that of Coleoloides typicalis e.g., ref. 16—which are about same size and have a circular cross section as the sediment-filled “siphuncles”1, figs. 2, 4b.The “siphuncles” are best interpreted as invaginated (cone-in-cone), sediment-filled, small conchs comparable to those in the “1 note submarginal “siphuncles”—which are not located very close to the ventral conch wall which would be more consistent with early cephalopods5. Far more concerning is that one of the “siphuncles” is not longitudinal but inclined1, fig. 4b, as shown by a more elongate cross section than the large conch it is in. This is expected in an inclined, invaginated tube.Hildenbrand et al.1, figs. 1d, 2c claim to illustrate cameral deposits on the inner wall of the “phragmocone” of the Bacon Cove material. However, the posited “cameral deposits” are not distinguishable in their very dark figures, while it must be noted that cameral deposits are unknown before the Ordovician e.g., ref. 3.Another feature known in cephalopod conchs are cameral deposits—calcareous shell material deposited on the interior chamber walls and septa of some conchs. Hildenbrand et al.1, Supp. Fig. 1, these are type specimens of a chimaera, not a biological organism. In their tentative cephalopod assignment, Hildenbrand et al.1 describe these chimaeras in terms specific to cephalopods —which means an a priori taxonomic assignment colors their specimen descriptions.This evidence is that the Bacon Cove “cephalopods” are chimaeras—an association of several taxa, none of which is a cephalopod. Although “holotype” and “paratypes” of an unnamed taxon are named and figured1. Thus, lack of crossed lamellar microhistology of septa is said to argue against a hyolith assignment1, but replacement of hyolith aragonite by calcite spar and loss of original microhistology is characteristic in Avalonia and in our Bacon Cove specimens6. Indeed, hyolith microhistology is only known from phosphatized, not calcite replaced, specimens20. Similarly, lack of an operculum supposedly precludes a hyolith assignment1; which really means Hildenbrand et al.1 did not find the readily detached “L.” cylindrica operculum21.Absence of evidence is used as evidence for a cephalopod assignment of the Bacon Cove specimens by Hildenbrand et al.Coleoloides? conchs) is claimed to support a cephalopod assignment of the Bacon Cove material1. However, this is incorrect; the common phosphatic connecting rings of Phanerozoic cephalopods are not a taxonomic feature but reflect secondary diagenesis of organic-rich connecting rings24. The phosphate is difficult to interpret in the Bacon Cove material: An EDX image of a 1.2 mm wide, purportedly double-walled, phosphatic “connecting ring” is figured1, fig. 3c, d, but a double-wall is absent in an optical image of the same(?) “ring,” which is only 0.5 mm wide1, fig. 2.Finally, phosphatic “siphuncles”/”connecting rings” , with Bayesian phylogenetic analysis showing a middle Late Cambrian–earliest Ordovician (Tremadocian) diversification of the class Cephalopoda into three major clades (subclasses) and several unassigned orders5. An earlier fossil record of the class remains unknown, with the Bacon Cove conchs confidently referable to the Hyolitha and not to the cephalopods. The middle Late Cambrian diversification of predatory cephalopods and appearance of diverse and abundant euconodonts marked the modernization of marine ecosystems and dawn of the Great Ordovician Diversification Interval26.In conclusion, Early Cambrian conchs from SE Newfoundland are not cephalopods. They are a chimaera composed of conchs of the common orthothecid hyolith ion Fig.  shows th