Bubble blasts! An adaptation for buoyancy regulation in shallow foraging gray whales.

Foraging efficiency is key to animal fitness. Consequently, animals evolved a variety of kinematic, morphological, physiological, and behavioral adaptations for efficient locomotion to reduce energy expenditure while moving to find, capture, and consume prey. Often suited to specific habitat and prey types, these adaptations correspond to the terrain or substrate the animal moves through. In aquatic systems, adaptations focus on overcoming drag, buoyancy, and hydrostatic forces. Buoyancy both benefits and hinders diving animals; in particular, shallow divers constantly contend with the costs of overcoming buoyancy to dive and maintain position. Pacific Coast Feeding Group (PCFG) gray whales forage in shallow habitats where they work against buoyancy to dive and feed using various foraging tactics. Bubble blasts (underwater exhalations) have been observed during several foraging tactics performed by PCFG whales. As exhalations aid buoyancy regulation in other diving animals, we hypothesize that bubble blasts are performed by longer, more buoyant whales in shallower water and that bubble blasts increase dive duration while accounting for size and tactic. We test our hypotheses using Bayesian linear mixed effects models and a 7-year dataset of drone footage containing concurrent individual morphological and behavioral data. We find that while headstanding - a stationary, head-down tactic - bubble blasts are performed by longer, more buoyant whales and extend the dive duration, whereas whales using forward-swimming tactics are less likely to bubble blast. Our results suggest that PCFG gray whales may use bubble blasts as a behavioral adaption to mitigate the cost of energetically expensive tactics in their shallow habitat foraging niche.

Identifying drivers of non-stationary climate-growth relationships of European beech.

The future performance of the widely abundant European beech (Fagus sylvatica L.) across its ecological amplitude is uncertain. Although beech is considered drought-sensitive and thus negatively affected by drought events, scientific evidence indicating increasing drought vulnerability under climate change on a cross-regional scale remains elusive. While evaluating changes in climate sensitivity of secondary growth offers a promising avenue, studies from productive, closed-canopy forests suffer from knowledge gaps, especially regarding the natural variability of climate sensitivity and how it relates to radial growth as an indicator of tree vitality. Since beech is sensitive to drought, we in this study use a drought index as a climate variable to account for the combined effects of temperature and water availability and explore how the drought sensitivity of secondary growth varies temporally in dependence on growth variability, growth trends, and climatic water availability across the species' ecological amplitude. Our results show that drought sensitivity is highly variable and non-stationary, though consistently higher at dry sites compared to moist sites. Increasing drought sensitivity can largely be explained by increasing climatic aridity, especially as it is exacerbated by climate change and trees' rank progression within forest communities, as (co-)dominant trees are more sensitive to extra-canopy climatic conditions than trees embedded in understories. However, during the driest periods of the 20th century, growth showed clear signs of being decoupled from climate. This may indicate fundamental changes in system behavior and be early-warning signals of decreasing drought tolerance. The multiple significant interaction terms in our model elucidate the complexity of European beech's drought sensitivity, which needs to be taken into consideration when assessing this species' response to climate change.

Minimal detectable change of gait and balance measures in older neurological patients: estimating the standard error of the measurement from before-after rehabilitation data thanks to the linear mixed-effects models.

BACKGROUND: Tracking gait and balance impairment in time is paramount in the care of older neurological patients. The Minimal Detectable Change (MDC), built upon the Standard Error of the Measurement (SEM), is the smallest modification of a measure exceeding the measurement error. Here, a novel method based on linear mixed-effects models (LMMs) is applied to estimate the standard error of the measurement from data collected before and after rehabilitation and calculate the MDC of gait and balance measures. METHODS: One hundred nine older adults with a gait impairment due to neurological disease (66 stroke patients) completed two assessment sessions before and after inpatient rehabilitation. In each session, two trials of the 10-meter walking test and the Timed Up and Go (TUG) test, instrumented with inertial sensors, have been collected. The 95% MDC was calculated for the gait speed, TUG test duration (TTD) and other measures from the TUG test, including the angular velocity peak (ωpeak) in the TUG test's turning phase. Random intercepts and slopes LMMs with sessions as fixed effects were used to estimate SEM. LMMs assumptions (residuals normality and homoscedasticity) were checked, and the predictor variable ln-transformed if needed. RESULTS: The MDC of gait speed was 0.13 m/s. The TTD MDC, ln-transformed and then expressed as a percentage of the baseline value to meet LMMs' assumptions, was 15%, i.e. TTD should be < 85% of the baseline value to conclude the patient's improvement. ωpeak MDC, also ln-transformed and expressed as the baseline percentage change, was 25%. CONCLUSIONS: LMMs allowed calculating the MDC of gait and balance measures even if the test-retest steady-state assumption did not hold. The MDC of gait speed, TTD and ωpeak from the TUG test with an inertial sensor have been provided. These indices allow monitoring of the gait and balance impairment, which is central for patients with an increased falling risk, such as neurological old persons. TRIAL REGISTRATION: NA.

Evaluation of partial volume correction and analysis of longitudinal [18F]GTP1 tau PET imaging in Alzheimer's disease using linear mixed-effects models.

Purpose: We evaluated the impact of partial volume correction (PVC) methods on the quantification of longitudinal [18F]GTP1 tau positron-emission tomography (PET) in Alzheimer's disease and the suitability of describing the tau pathology burden temporal trajectories using linear mixed-effects models (LMEM). Methods: We applied van Cittert iterative deconvolution (VC), 2-compartment, and 3-compartment, and the geometric transfer matrix plus region-based voxelwise methods to data acquired in an Alzheimer's disease natural history study over 18 months at a single imaging site. We determined the optimal PVC method by comparing the standardized uptake value ratio change (%ΔSUVR) between diagnostic and tau burden-level groups and the longitudinal repeatability derived from the LMEM. The performance of LMEM analysis for calculating %ΔSUVR was evaluated in a natural history study and in a multisite clinical trial of semorinemab in prodromal to mild Alzheimer's disease by comparing results to traditional per-visit estimates. Results: The VC, 2-compartment, and 3-compartment PVC methods had similar performance, whereas region-based voxelwise overcorrected regions with a higher tau burden. The lowest within-subject variability and acceptable group separation scores were observed without PVC. The LMEM-derived %ΔSUVR values were similar to the per-visit estimates with lower variability. Conclusion: The results indicate that the tested PVC methods do not offer a clear advantage or improvement over non-PVC images for the quantification of longitudinal [18F]GTP1 PET data. LMEM offers a robust framework for the longitudinal tau PET quantification with low longitudinal test-retest variability. Clinical trial registration: NCT02640092 and NCT03289143.

The laboratory parameters-derived CoLab score as an indicator of the host response in ICU COVID-19 patients decreases over time: a prospective cohort study.

The CoLab score was developed and externally validated to rule out COVID-19 among suspected patients presenting at the emergency department. We hypothesized a within-patient decrease in the CoLab score over time in an intensive care unit (ICU) cohort. Such a decrease would create the opportunity to potentially rule out the need for isolation when the infection is overcome. Using linear mixed-effects models, data from the Maastricht Intensive Care COVID (MaastrICCht) cohort were used to investigate the association between time and the CoLab score. Models were adjusted for sex, APACHE II score, ICU mortality, and daily SOFA score. The CoLab score decreased by 0.30 points per day (95% CI - 0.33 to - 0.27), independent of sex, APACHE II, and Mortality. With increasing SOFA score over time, the CoLab score decreased more strongly (- 0.01 (95% CI - 0.01 to - 0.01) additional decrease per one-point increase in SOFA score.) The CoLab score decreased in ICU patients on mechanical ventilation for COVID-19, with a one-point reduction per three days, independent of sex, APACHE II, and ICU mortality, and somewhat stronger with increasing multi-organ failure over time. This suggests that the CoLab score would decrease below a threshold where COVID-19 can be excluded.

Oxytocin Modifies the Excitability and the Action Potential Shape of the Hippocampal CA1 GABAergic Interneurons.

Oxytocin (OT) is a neuropeptide that modulates social-related behavior and cognition in the central nervous system of mammals. In the CA1 area of the hippocampus, the indirect effects of the OT on the pyramidal neurons and their role in information processing have been elucidated. However, limited data are available concerning the direct modulation exerted by OT on the CA1 interneurons (INs) expressing the oxytocin receptor (OTR). Here, we demonstrated that TGOT (Thr4,Gly7-oxytocin), a selective OTR agonist, affects not only the membrane potential and the firing frequency but also the neuronal excitability and the shape of the action potentials (APs) of these INs in mice. Furthermore, we constructed linear mixed-effects models (LMMs) to unravel the dependencies between the AP parameters and the firing frequency, also considering how TGOT can interact with them to strengthen or weaken these influences. Our analyses indicate that OT regulates the functionality of the CA1 GABAergic INs through different and independent mechanisms. Specifically, the increase in neuronal firing rate can be attributed to the depolarizing effect on the membrane potential and the related enhancement in cellular excitability by the peptide. In contrast, the significant changes in the AP shape are directly linked to oxytocinergic modulation. Importantly, these alterations in AP shape are not associated with the TGOT-induced increase in neuronal firing rate, being themselves critical for signal processing.

The CoLab score is associated with SARS-CoV-2 viral load during admission in individuals admitted to the intensive care unit: the CoLaIC cohort study.

OBJECTIVES: The present study examines the temporal association between the changes in SARS-CoV-2 viral load during infection and whether the CoLab-score can facilitate de-isolation. METHODS: Nasal swabs and blood samples were collected from ICU-admitted SARS-CoV-2 positive patients at Maastricht UMC+ from March 25, 2020 to October 1, 2021. The CoLab-score was calculated based on 10 blood parameters and age and can range from -43 to 6. Three mixed effects analyses compared patient categories based on initial PCR Ct values (low; Ct≤20, mid; 20>Ct≤30, high; Ct>30), serial PCR Ct values to CoLab-scores over time, and the association between within-patient delta Ct values and CoLab-scores. RESULTS: In 324 patients, the median Ct was 33, and the median CoLab-score was -1.78. Mid (n=110) and low (n=41) Ct-categories had higher CoLab-scores over time (+0.60 points, 95 % CI; 0.04-1.17, and +0.28 points, 95 % CI -0.49 to 1.04) compared to the high Ct (n=87) category. Over time, higher serial Ct values were associated with lower serial CoLab-scores, decreasing by -0.07 points (95 % CI; -0.11 to -0.02) per day. Increasing delta Ct values were associated with a decreasing delta CoLab-score of -0.12 (95 % CI; -0.23; -0.01). CONCLUSIONS: The study found an association between lower viral load on admission and reduced CoLab-score. Additionally, a decrease in viral load over time was associated with a decrease in CoLab-score. Therefore, the CoLab-score may make patient de-isolation an option based on the CoLab-score.

Enrollment forecast for clinical trials at the portfolio planning phase based on site-level historical data.

An accurate forecast of a clinical trial enrollment timeline at the planning phase is of great importance to both corporate strategic planning and trial operational excellence. The naive approach often calculates an average enrollment rate from historical data and generates an inaccurate prediction based on a linear trend with the average rate. Under the traditional framework of a Poisson-Gamma model, site activation delays are often modeled with either fixed initiation time or a simple random distribution while incorporating the user-provided site planning information to achieve good forecast accuracy. However, such user-provided information is not available at the early portfolio planning stage. We present a novel statistical approach based on generalized linear mixed-effects models and the use of non-homogeneous Poisson processes through the Bayesian framework to model the country initiation, site activation, and subject enrollment sequentially in a systematic fashion. We validate the performance of our proposed enrollment modeling framework based on a set of 25 preselected studies from four therapeutic areas. Our modeling framework shows a substantial improvement in prediction accuracy in comparison to the traditional statistical approach. Furthermore, we show that our modeling and simulation approach calibrates the data variability appropriately and gives correct coverage rates for prediction intervals of various nominal levels. Finally, we demonstrate the use of our approach to generate the predicted enrollment curves through time with confidence bands overlaid.

lmeEEG: Mass linear mixed-effects modeling of EEG data with crossed random effects.

BACKGROUND: Mixed-effects models are the current standard for the analysis of behavioral studies in psycholinguistics and related fields, given their ability to simultaneously model crossed random effects for subjects and items. However, they are hardly applied in neuroimaging and psychophysiology, where the use of mass univariate analyses in combination with permutation testing would be too computationally demanding to be practicable with mixed models. NEW METHOD: Here, we propose and validate an analytical strategy that enables the use of linear mixed models (LMM) with crossed random intercepts in mass univariate analyses of EEG data (lmeEEG). It avoids the unfeasible computational costs that would arise from massive permutation testing with LMM using a simple solution: removing random-effects contributions from EEG data and performing mass univariate linear analysis and permutations on the obtained marginal EEG. RESULTS: lmeEEG showed excellent performance properties in terms of power and false positive rate. COMPARISON WITH EXISTING METHODS: lmeEEG overcomes the computational costs of standard available approaches (our method was indeed more than 300 times faster). CONCLUSIONS: lmeEEG allows researchers to use mixed models with EEG mass univariate analyses. Thanks to the possibility offered by the method described here, we anticipate that LMM will become increasingly important in neuroscience. Data and codes are available at osf.io/kw87a. The codes and a tutorial are also available at github.com/antovis86/lmeEEG.

A novel MRI-based volumetric index for monitoring the motor symptoms in Parkinson's disease.

BACKGROUND: Conventional MRI scans have limited usefulness in monitoring Parkinson's disease as they typically do not show any disease-specific brain abnormalities. This study aimed to identify an imaging biomarker for tracking motor symptom progression by using a multivariate statistical approach that can combine gray matter volume information from multiple brain regions into a single score specific to each PD patient. METHODS: A cohort of 150 patients underwent MRI at baseline and had their motor symptoms tracked for up to 10 years using MDS-UPDRS-III, with motor symptoms focused on total and subscores, including rigidity, bradykinesia, postural instability, and gait disturbances, resting tremor, and postural-kinetic tremor. Gray matter volume extracted from MRI data was summarized into a patient-specific summary score using Mahalanobis distance, MGMV. MDS-UPDRS-III's progression and its association with MGMV were modeled via linear mixed-effects models over 5- and 10-year follow-up periods. RESULTS: Over the 5-year follow-up, there was a significant increase (P < 0.05) in MDS-UPDRS-III total and subscores, except for postural-kinetic tremor. Over the 10-year follow-up, all MDS-UPDRS-III scores increased significantly (P < 0.05). A higher baseline MGMV was associated with a significant increase in MDS-UPDRS-III total, bradykinesia, postural instability and gait disturbances, and resting tremor (P < 0.05) over the 5-year follow-up, but only with total, bradykinesia, and postural instability and gait disturbances during the 10-year follow-up (P < 0.05). CONCLUSIONS: Higher MGMV scores were linked to faster motor symptom progression, suggesting it could be a valuable marker for clinicians monitoring Parkinson's disease over time.

Sodium evolution in hyponatraemia: a mixed effects model analysis of the Hyponatraemia Registry.

OBJECTIVE: Achieving recommended targets of sodium correction is challenging to physicians treating hyponatraemia. Plasma sodium has to be increased effectively, yet overcorrection must be prevented. This is often hampered by a high variability of responses to treatment. Here, we sought to delineate factors influencing sodium evolution. DESIGN: We retrospectively analysed 3460 patients from the multinational Hyponatraemia Registry comprising a wide range of hyponatraemia aetiologies and treatment strategies. METHODS: Multivariable linear mixed effects models were applied to identify predictors of plasma sodium evolution within the first 24 h of treatment. RESULTS: Evolution of sodium levels over time showed a curvilinear pattern with steeper rise at earlier time points. Baseline sodium showed the most pronounced impact with an additional increment of 3.12 mEq/L for every 10 mEq/L initial sodium reduction. With sodium increments of 1.9 mEq/L and 1.4 mEq/L per 24 h, respectively, the entities hypovolaemic and thiazide-associated hyponatraemia were independent factors for sodium evolution. Therapeutic regimens using hypertonic saline (4.6 mEq/L/24 h), tolvaptan (3.4 mEq/L/24 h), or combination therapy (2.6 mEq/L/24 h) were also associated with a significantly larger sodium rise when compared with no active treatment. CONCLUSIONS: Choice and dosing of active hyponatraemia therapy should be adjusted not only according to aetiology but most importantly to pretreatment sodium. Although counterintuitive, less aggressive therapy in more profound hyponatraemia might be safer but yet effective at least in less severe cases.

An Iodine Balance Study in Chinese School-age Children.

CONTEXT: Few iodine balance studies have been conducted in school-age children. OBJECTIVE: This study aimed to conduct an iodine balance study in school-age children. METHODS: We measured daily iodine intake, excretion, and retention for 3 consecutive days without any dietary interventions in school-age children. Linear mixed-effects models were used to fit the relationship between total iodine intake and iodine retention. RESULTS: 29 children aged 7-12 years (mean age 10.2 ± 1.4 years) with normal thyroid function and thyroid volume were recruited. The 0 balance value (iodine intake = iodine excretion, iodine retention = 0 µg/day) shifted with iodine intake in an iodine sufficient population. The 0 balance value for school-age children with an iodine intake of 235 (133, 401) µg/day is 164 µg/day. Children aged 7-12 years with iodine intake >400 µg/day were almost all in a positive iodine state. CONCLUSION: An iodine intake of 235 (133, 401) µg/day for children aged 7-10 years achieved a 0 balance value of 164 µg/day. Long-term iodine intake of >400 µg/day is not recommended.

Optimal dosing of gliclazide-A model-based approach.

Gliclazide was approved as a treatment for type 2 diabetes in an era before model-based drug development, and consequently, the recommended doses were not optimised with modern methods. To investigate various dosing regimens of gliclazide, we used publicly available data to characterise the dose-response relationship using pharmacometric models. A literature search identified 21 published gliclazide pharmacokinetic (PK) studies with full profiles. These were digitised, and a PK model was developed for immediate- (IR) and modified-release (MR) formulations. Data from a gliclazide dose-ranging study of postprandial glucose were used to characterise the concentration-response relationship using the integrated glucose-insulin model. Simulations from the full model showed that the maximum effect was 44% of the patients achieving HbA1c <7%, with 11% experiencing glucose <3 mmol/L and the most sensitive patients (i.e., 5% most extreme) experiencing 35 min of hypoglycaemia. Simulations revealed that the recommended IR dose (320 mg) was appropriate with no efficacy gain with increased dose. However, the recommended dose for the MR formulation may be increased to 270 mg, with more patients achieving HbA1c goals (i.e., HbA1c <7%) without a hypoglycaemic risk higher than the resulting risk from the recommended IR dose.

Genetic association models are robust to common population kinship estimation biases.

Common genetic association models for structured populations, including principal component analysis (PCA) and linear mixed-effects models (LMMs), model the correlation structure between individuals using population kinship matrices, also known as genetic relatedness matrices. However, the most common kinship estimators can have severe biases that were only recently determined. Here we characterize the effect of these kinship biases on genetic association. We employ a large simulated admixed family and genotypes from the 1000 Genomes Project, both with simulated traits, to evaluate key kinship estimators. Remarkably, we find practically invariant association statistics for kinship matrices of different bias types (matching all other features). We then prove using statistical theory and linear algebra that LMM association tests are invariant to these kinship biases, and PCA approximately so. Our proof shows that the intercept and relatedness effect coefficients compensate for the kinship bias, an argument that extends to generalized linear models. As a corollary, association testing is also invariant to changing the reference ancestral population of the kinship matrix. Lastly, we observed that all kinship estimators, except for popkin ratio-of-means, can give improper non-positive semidefinite matrices, which can be problematic although some LMMs handle them surprisingly well, and condition numbers can be used to choose kinship estimators. Overall, we find that existing association studies are robust to kinship estimation bias, and our calculations may help improve association methods by taking advantage of this unexpected robustness, as well as help determine the effects of kinship bias in related problems.

Model-based clustering of high-dimensional longitudinal data via regularization.

We propose a model-based clustering method for high-dimensional longitudinal data via regularization in this paper. This study was motivated by the Trial of Activity in Adolescent Girls (TAAG), which aimed to examine multilevel factors related to the change of physical activity by following up a cohort of 783 girls over 10 years from adolescence to early adulthood. Our goal is to identify the intrinsic grouping of subjects with similar patterns of physical activity trajectories and the most relevant predictors within each group. The previous analyses conducted clustering and variable selection in two steps, while our new method can perform the tasks simultaneously. Within each cluster, a linear mixed-effects model (LMM) is fitted with a doubly penalized likelihood to induce sparsity for parameter estimation and effect selection. The large-sample joint properties are established, allowing the dimensions of both fixed and random effects to increase at an exponential rate of the sample size, with a general class of penalty functions. Assuming subjects are drawn from a Gaussian mixture distribution, model effects and cluster labels are estimated via a coordinate descent algorithm nested inside the Expectation-Maximization (EM) algorithm. Bayesian Information Criterion (BIC) is used to determine the optimal number of clusters and the values of tuning parameters. Our numerical studies show that the new method has satisfactory performance and is able to accommodate complex data with multilevel and/or longitudinal effects.

Individual and Family Determinants for Quality of Life in Parents of Children with Inborn Errors of Metabolism Requiring a Restricted Diet: A Multilevel Analysis Approach.

OBJECTIVE: The objective of this study was to compare the quality of life (QoL) for parents of children with inborn errors of metabolism (IEMs) requiring a restricted diet with French population norms and investigate parental QoL determinants. STUDY DESIGN: This cross-sectional study included mothers and/or fathers of children < 18 years of age affected by IEMs requiring a restricted diet (except phenylketonuria) from January 2015 to December 2017. Parents' QoL was assessed using the World Health Organization Quality of Life BREF questionnaire and compared with age- and sex-matched reference values from the French general population. Linear mixed models were used to examine the effects of demographic, socioeconomic, disease-related, and psychocognitive factors on parental QoL, according to a 2-level regression model considering individuals (parents) nested within families. RESULTS: Of the 1156 parents invited to participate, 785 (68%) were included. Compared with the general population, parents of children with IEMs requiring a restricted diet reported a lower QoL in physical and social relationship domains but a higher QoL in the psychological domain. In the multivariate analysis, characteristics associated with poorer parental QoL included both parent-related factors (being a father, older age, more educated parent, nonworking parent, greater anxiety, seeking more social support, and using less positive thinking and problem-solving coping strategies) and family-related factors (disease complications, increased number of hospital medical providers, child's younger age, single-parent family, and lower family material wealth). CONCLUSION: Parents of children with IEMs requiring a restricted diet reported poorer QoL in physical and social relationship domains than population norms. Psychocognitive factors, beyond disease-specific and family-related characteristics, were the most important determinants influencing parental QoL and may represent essential aspects for interventions. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov: NCT02552784.

Blood Analytes as Biomarkers of Mechanisms Involved in Alzheimer's Disease Progression.

Alzheimer's disease (AD) is the leading cause of dementia, but the pathogenetic factors are not yet well known, and the relationships between brain and systemic biochemical derangements and disease onset and progression are unclear. We aim to focus on blood biomarkers for an accurate prognosis of the disease. We used a dataset characterized by longitudinal findings collected over the past 10 years from 90 AD patients. The dataset included 277 observations (both clinical and biochemical ones, encompassing blood analytes encompassing routine profiles for different organs, together with immunoinflammatory and oxidative markers). Subjects were grouped into four severity classes according to the Clinical Dementia Rating (CDR) Scale: mild (CDR = 0.5 and CDR = 1), moderate (CDR = 2), severe (CDR = 3) and very severe (CDR = 4 and CDR = 5). Statistical models were used for the identification of potential blood markers of AD progression. Moreover, we employed the Pathfinder tool of the Reactome database to investigate the biological pathways in which the analytes of interest could be involved. Statistical results reveal an inverse significant relation between four analytes (high-density cholesterol, total cholesterol, iron and ferritin) with AD severity. In addition, the Reactome database suggests that such analytes could be involved in pathways that are altered in AD progression. Indeed, the identified blood markers include molecules that reflect the heterogeneous pathogenetic mechanisms of AD. The combination of such blood analytes might be an early indicator of AD progression and constitute useful therapeutic targets.

The association between circadian rhythm of cortisol and shift work regularity among midwives-A multicenter study in Southeast China.

Objective: This article aims to explore the association between the trends of cortisol rhythm and the regularity of shift work among midwives. Methods: Midwives from six Southeast Chinese hospitals were recruited through cluster sampling in a multi-center cross-sectional study. Urine samples were collected half an hour after waking up, at 11:00, 19:00, and 23:00 on two consecutive days in a longitudinal cohort. The urinary cortisol was assayed by the chemiluminescence method. Results: A total of 86 midwives were included in this study, contributing 688 cortisol samples. The midwives displayed a circadian rhythm in cortisol secretion, with zeniths in the morning and nadirs in the evening. The trend of the first day was repeated on the second day. Although the total working hours per week of the two groups, namely the regular shift group (N = 43) and the irregular shift group (N = 43), were the same, significant main effects of groups (F = 62.569, p < 0.001), time (F = 45.304, p < 0.001), and group-by-time interaction (F = 226.695, p < 0.001) were indicated through linear mixed models. The main effect of day was not statistically significant, with F = 0.105 and p = 0.746. The fluctuation range of cortisol curve in the group with irregular schedules was slightly lower than that in the group with regular schedules. Conclusion: Our results may indicate that cortisol was more inhibited in midwives with irregular shift patterns than those with regular shift patterns. It is necessary to further study the relationship between cortisol rhythm and patterns of midwives' shifts in future so as to lay a foundation for hospital managers to develop a more reasonable scheduling system for midwives with the further purpose to minimize their occupational fatigue and ensure the safety of mothers and infants.

A Multilevel Analysis of the Associated and Determining Factors of TB among Adults in South Africa: Results from National Income Dynamics Surveys 2008 to 2017.

TB is preventable and treatable but remains the leading cause of death in South Africa. The deaths due to TB have declined, but in 2017, around 322,000 new cases were reported in the country. The need to eradicate the disease through research is increasing. This study used population-based National Income Dynamics Survey data (Wave 1 to Wave 5) from 2008 to 2017. By determining the simultaneous multilevel and individual-level predictors of TB, this research examined the factors associated with TB-diagnosed individuals and to what extent the factors vary across such individuals belonging to the same province in South Africa for the five waves. Multilevel logistic regression models were fitted using frequentist and Bayesian techniques, and the results were presented as odds ratios with statistical significance set at p < 0.05. The results obtained from the two approaches were compared and discussed. Findings reveal that the TB factors that prevailed consistently from wave 1 to wave 5 were marital status, age, gender, education, smoking, suffering from other diseases, and consultation with a health practitioner. Also, over the years, the single males aged 30-44 years suffering from other diseases with no education were highly associated with TB between 2008 and 2017. The methodological findings were that the frequentist and Bayesian models resulted in the same TB factors. Both models showed that some form of variation in TB infections is due to the different provinces these individuals belonged. Variation in TB patients within the same province over the waves was minimal. We conclude that demographic and behavioural factors also drive TB infections in South Africa. This research supports the existing findings that controlling the social determinants of health will help eradicate TB.

Impact of model misspecification on model-based tests in PK studies with parallel design: real case and simulation studies.

This article evaluates the performance of pharmacokinetic (PK) equivalence testing between two formulations of a drug through the Two-One Sided Tests (TOST) by a model-based approach (MB-TOST), as an alternative to the classical non-compartmental approach (NCA-TOST), for a sparse design with a few time points per subject. We focused on the impact of model misspecification and the relevance of model selection for the reference data. We first analysed PK data from phase I studies of gantenerumab, a monoclonal antibody for the treatment of Alzheimer's disease. Using the original rich sample data, we compared MB-TOST to NCA-TOST for validation. Then, the analysis was repeated on a sparse subset of the original data with MB-TOST. This analysis inspired a simulation study with rich and sparse designs. With rich designs, we compared NCA-TOST and MB-TOST in terms of type I error and study power. With both designs, we explored the impact of misspecifying the model on the performance of MB-TOST and adding a model selection step. Using the observed data, the results of both approaches were in general concordance. MB-TOST results were robust with sparse designs when the underlying PK structural model was correctly specified. Using the simulated data with a rich design, the type I error of NCA-TOST was close to the nominal level. When using the simulated model, the type I error of MB-TOST was controlled on rich and sparse designs, but using a misspecified model led to inflated type I errors. Adding a model selection step on the reference data reduced the inflation. MB-TOST appears as a robust alternative to NCA-TOST, provided that the PK model is correctly specified and the test drug has the same PK structural model as the reference drug.