Sex diversity in the 21st century: Concepts, frameworks, and approaches for the future of neuroendocrinology

Sex is ubiquitous and variable throughout the animal kingdom. Historically, scientists have used reductionist methodologies that rely on a priori sex categorizations, in which two discrete sexes are inextricably linked with gamete type. However, this binarized operationalization does not adequately reflect the diversity of sex observed in nature. This is due, in part, to the fact that sex exists across many levels of biological analysis, including genetic, molecular, cellular, morphological, behavioral, and population levels. Furthermore, the biological mechanisms governing sex are embedded in complex networks that dynamically interact with other systems. To produce the most accurate and scientifically rigorous work examining sex in neuroendocrinology and to capture the full range of sex variability and diversity present in animal systems, we must critically assess the frameworks, experimental designs, and analytical methods used in our research. In this perspective piece, we first propose a new conceptual framework to guide the integrative study of sex. Then, we provide practical guidance on research approaches for studying sex-associated variables, including factors to consider in study design, selection of model organisms, experimental methodologies, and statistical analyses. We invite fellow scientists to conscientiously apply these modernized approaches to advance our biological understanding of sex and to encourage academically and socially responsible outcomes of our work. By expanding our conceptual frameworks and methodological approaches to the study of sex, we will gain insight into the unique ways that sex exists across levels of biological organization to produce the vast array of variability and diversity observed in nature.

Multivariable models in orthopaedic research: a methodological review of covariate selection and causal relationships

OBJECTIVES

The aim of this study was to evaluate the methods used for including or excluding covariates in a multivariable model and to find out how common is the Table 2 Fallacy in studies recently published in high-quality orthopaedic journals.

METHODS

A systematic review was conducted in the MEDLINE database. We included all studies that presented the results of a multivariable model in a table and published in seven orthopaedic journals with the highest ranked impact factors in 2019.

RESULTS

Table 2 Fallacy was found in 67% (129/193) of the evaluated studies in which a multivariable model was used. Only 16% (31/193) of all studies had included the variables based on causal inference. Furthermore, only three of these studies used causal diagrams to illustrate the causal inference. Altogether, 35% (67/193) of the studies included variables based on statistical methods.

CONCLUSIONS

Confounder selection and the interpretation of the results of the multivariable model showed notable challenges in orthopaedic studies recently published in the top orthopaedic journals. Based on the results of our review, it seems that more education in statistics and increased knowledge is required to decrease the occurrence of these statistical issues in orthopaedic research.

Multivariable non-minimum state space model predictive control based on disturbance observer

In order to suppress the influence of lumped system disturbance, such as external disturbance and internal disturbance caused by model mismatch and coupling between variables, more effectively, a multivariable non-minimum state space predictive control method based on disturbance observer (MNMSSPC-D) is proposed in this paper. Most of the existing methods based on the feedback control and feedforward compensation cannot guarantee optimal output. Unlike the existing methods, the proposed method extends the estimated disturbance and output variables into the state variables, forming a multivariable non-minimum state space (MNMSS) prediction model, and then uses the rolling optimization principle in predictive control to design the controller based on the formed prediction model. The main advantages of the proposed method are that the state can be guaranteed to be available to the MNMSS model and the optimal control performance and anti-disturbance ability of system can be obtained by the designed controller. The proposed MNMSSPC-D method is verified by the simulation with a heavy oil fractionator.

A New Framework for the Assessment of Cerebral Hemodynamics Regulation in Neonates Using NIRS

We present a new framework for the assessment of cerebral hemodynamics regulation (CHR) in neonates using near-infrared spectroscopy (NIRS). In premature infants, NIRS measurements have been used as surrogate variables for cerebral blood flow (CBF) in the assessment of cerebral autoregulation (CA). However, NIRS measurements only reflect changes in CBF under constant changes in arterial oxygen saturation (SaO2). This condition is unlikely to be met at the bedside in the NICU. Additionally, CA is just one of the different highly coupled mechanisms that regulate brain hemodynamics. Traditional methods for the assessment of CA do not take into account the multivariate nature of CHR, producing inconclusive results. In this study we propose a newly developed multivariate methodology for the assessment of CHR. This method is able to effectively decouple the influences of SaO2 from the NIRS measurements, and at the same time, produces scores indicating the strength of the coupling between the systemic variables and NIRS recordings. We explore the use of this method, and its derived scores, for the monitoring of CHR using data from premature infants who developed a grade III-IV intra-ventricular hemorrhage during the first 3 days of life.

Early predictors of remission in newly diagnosed epilepsy: a systematic approach to reviewing prognostic factor studies

BACKGROUND

It is necessary to select a range of consistently identified prognostic factors from exploratory studies to include in multivariate models of confirmatory studies. We illustrate a systematic approach to selecting consistently identified prognostic factors using the example of predictors of remission in newly diagnosed epilepsy.

METHODS

Medline and Embase were searched for reports of cohort studies enrolling at least 100 people with epilepsy within 1 year of diagnosis, and followed up for at least 1 year. We included studies that identified predictors of remission after adjusting for confounders using multivariate regression analysis. To identify consistent predictors a chart was designed to list the variables considered for inclusion in each model and those retained in more than one model from different cohorts were deemed to be consistent.

RESULTS

Remission off medication was less likely if there was more than one seizure between 6 and 12 months on medication and if there was comorbid intellectual disability in childhood onset epilepsy. The likelihood of remission on or off medication reduces with mixed seizure types at onset, intellectual disability, symptomatic aetiology, and also with increasing number of seizures before diagnosis or in the first 6 months after diagnosis.

CONCLUSION

A greater number of seizures before diagnosis and early in treatment, intellectual disability, and symptomatic aetiology are consistent predictors of less likelihood of remission. This suggests that early identification, diagnosis of epilepsy, and seizure control should be the primary aim of medical intervention, and that these predictors should be included in future confirmatory studies of prognostic factors of remission in newly diagnosed epilepsy.