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Introduction: Enhanced creativity is often cited as an effect of microdosing (taking repeated low doses of a psychedelic drug). There have been recent efforts to validate the reported effects of microdosing, however creativity remains a difficult construct to quantify., Objectives: The current study aimed to assess microdosing's effects on creativity using a multimodal battery of tests as part of a randomised controlled trial of microdosing lysergic acid diethylamide (LSD)., Methods: Eighty healthy adult males were given 10 µg doses of LSD or placebo every third day for six weeks (14 total doses). Creativity tasks were administered at a drug-free baseline session, at a first dosing session during the acute phase of the drug's effects, and in a drug-free final session following the six-week microdosing regimen. Creativity tasks were the Alternate Uses Test (AUT), Remote Associates Task (RAT), Consensual Assessment Technique (CAT), and an Everyday Problem-Solving Questionnaire (EPSQ)., Results: No effect of drug by time was found on the AUT, RAT, CAT, or EPSQ. Baseline vocabulary skill had a significant effect on AUT and RAT scores., Conclusions: Despite participants reporting feeling more creative on dose days, objective measurement found no acute or durable effects of the microdosing protocol on creativity. Possible explanations of these null findings are that laboratory testing conditions may negatively affect ability to detect naturalistic differences in creative performance, the tests available do not capture the facets of creativity that are anecdotally affected by microdosing, or that reported enhancements of creativity are placebo effects., (© 2024. The Author(s).)

Background: Major depressive disorder (MDD) poses a significant global health burden with available treatments limited by inconsistent efficacy and notable side effects. Classic psychedelics, including lysergic acid diethylamide (LSD), have garnered attention for their potential in treating psychiatric disorders. Microdosing, the repeated consumption of sub-hallucinogenic doses of psychedelics, has emerged as a self-treatment approach for depression within lay communities. Building upon preliminary evidence and the successful completion of an open-label pilot trial of microdosing LSD for depression (LSDDEP1), this protocol outlines a phase 2b randomised controlled trial (LSDDEP2). The main objective of LSDDEP2 is to assess the modification of depressive symptoms, measured by the Montgomery-Åsberg Depression Rating Scale (MADRS), following a regimen of LSD microdoses versus placebo., Methods: This is a randomised, double-dummy, triple-blind, active placebo-controlled, parallel groups trial of LSD microdosing in patients meeting DSM-5 criteria for major depressive disorder. Participants will undergo an 8-week LSD microdosing regimen using the titratable MB-22001 formulation taking two doses a week. All doses will be self-administered at home and will be titratable from 4 to 20 μg based on subjective perception and tolerability. In addition to depression symptoms, outcome will include psychiatric and personality inventories, sleep and activity tracking, electroencephalography (EEG), blood biomarkers, semi-structured interviews, and safety (e.g. adverse event, laboratory exam) measures., Discussion: This study will be the first randomised controlled trial to administer controlled microdoses of LSD for treatment of MDD in participants' naturalistic environment. The measures included are designed to assess the drug's safety, mechanism, and treatment efficacy over placebo in this population. The results of this study will be important for assessing the viability of psychedelic microdosing as an additional treatment option and for informing the direction of future clinical trials., Trial Registration: ANZCTR, ACTRN12624000128594. Prospectively Registered on 13 February 2024., (© 2024. The Author(s).)

Primary hyperparathyroidism (PHPT) and subsequent hypercalcemia have been reported to be associated with psychosis. Here we report the case of a 28-year-old cannabis using male with his first contact with psychiatric care because of mood instability, bizarre behavior, and poor ability to carry out activities of daily living. Hypercalcemia was identified, and a subsequent endocrine workup confirmed PHPT. After parathyroidectomy, there was no longer any need for antipsychotic or other psychotropic medications; the report emphasizes the importance of considering organic causes, such as hyperparathyroidism, in patients presenting with psychotic-like symptoms, including in the setting of substance use disorder. Prompt recognition and appropriate management of the underlying condition are crucial for optimizing patient outcomes., Competing Interests: There are no conflicts of interest to disclose., (Copyright © 2024 Rachael J. Murphy et al.)

Taking regular low doses of psychedelic drugs (microdosing) is a practice that has drawn recent scientific and media attention for its potential psychotherapeutic effects. Yet, controlled studies evaluating this practice have lagged. Here, we review recent evidence focusing on studies that were conducted with rigorous experimental control. Studies conducted under laboratory settings using double-blind placebo-controlled procedures and investigator-supplied drug were compiled. The review includes demographic characteristics of participants and dependent measures such as physiological, behavioral, and subjective effects of the drugs. Review criteria were met by 14 studies, all of which involved acute or repeated low (5-20 μg) doses of lysergic acid diethylamide (LSD). Acute microdoses of LSD dose-dependently altered blood pressure, sleep, neural connectivity, social cognition, mood, and perception of pain and time. Perceptible drug effects were reported at doses of 10 to 20 μg but not 5 μg. No serious adverse effects were reported. Repeated doses of LSD did not alter mood or cognition on any of the measures studied. The findings suggest that low doses of LSD are safe and produce acute behavioral and neural effects in healthy adults. Further studies are warranted to extend these findings to patient samples and to other psychedelic drugs and to investigate microdosing as a potential pharmacological treatment for psychiatric disorders., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Partial differential equation (PDE) models are often used to study biological phenomena involving movement-birth-death processes, including ecological population dynamics and the invasion of populations of biological cells. Count data, by definition, is non-negative, and count data relating to biological populations is often bounded above by some carrying capacity that arises through biological competition for space or nutrients. Parameter estimation, parameter identifiability, and making model predictions usually involves working with a measurement error model that explicitly relating experimental measurements with the solution of a mathematical model. In many biological applications, a typical approach is to assume the data are normally distributed about the solution of the mathematical model. Despite the widespread use of the standard additive Gaussian measurement error model, the assumptions inherent in this approach are rarely explicitly considered or compared with other options. Here, we interpret scratch assay data, involving migration, proliferation and delays in a population of cancer cells using a reaction-diffusion PDE model. We consider relating experimental measurements to the PDE solution using a standard additive Gaussian measurement error model alongside a comparison to a more biologically realistic binomial measurement error model. While estimates of model parameters are relatively insensitive to the choice of measurement error model, model predictions for data realisations are very sensitive. The standard additive Gaussian measurement error model leads to biologically inconsistent predictions, such as negative counts and counts that exceed the carrying capacity across a relatively large spatial region within the experiment. Furthermore, the standard additive Gaussian measurement error model requires estimating an additional parameter compared to the binomial measurement error model. In contrast, the binomial measurement error model leads to biologically plausible predictions and is simpler to implement. We provide open source Julia software on GitHub to replicate all calculations in this work, and we explain how to generalise our approach to deal with coupled PDE models with several dependent variables through a multinomial measurement error model, as well as pointing out other potential generalisations by linking our work with established practices in the field of generalised linear models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Microdosing psychedelics is a phenomenon with claimed cognitive benefits that are relatively untested clinically. Pre-clinically, psychedelics have demonstrated enhancing effects on neuroplasticity, which cannot be measured directly in humans, but may be indexed by non-invasive electroencephalography (EEG) paradigms. This study used a visual long-term potentiation (LTP) EEG paradigm to test the effects of microdosed lysergic acid diethylamide (LSD) on neural plasticity, both acutely while on the drug and cumulatively after microdosing every third day for six weeks. Healthy adult males (n = 80) completed the visual LTP paradigm at baseline, 2.5 h following a dose of 10 µg of LSD or inactive placebo, and 6 weeks later after taking 14 repeated microdoses. Visually induced LTP was used as indirect index of neural plasticity. Surface level event-related potential (ERPs) based analyses are presented alongside dynamic causal modelling of the source localised data using a generative thalamocortical model (TCM) of visual cortex to elucidate underlying synaptic circuitry., Results: Event-related potential (ERP) analyses of N1b and P2 components did not show evidence of changes in visually induced LTP by LSD either acutely or after 6 weeks of regular dosing. However modelling the complete timecourse of the ERP with the TCM demonstrated changes in laminar connectivity in primary visual cortex. This primarily included changes to self-gain and inhibitory input parameters acutely. Layer 2/3 to layer 5 excitatory connectivity was also different between LSD and placebo groups. After regular dosing only excitatory input from layer 2/3 into layer 5 and inhibitory input into layer 4 were different between groups., Conclusions: Without modulation of the ERPs it is difficult to relate the findings to other studies visually inducing LTP. It also indicates the classic peak analysis may not be sensitive enough to demonstrate evidence for changes in LTP plasticity in humans at such low doses. The TCM provides a more sensitive approach to assessing changes to plasticity as differences in plasticity mediated laminar connectivity were found between the LSD and placebo groups., Trial Registration:  ANZCTR registration number ACTRN12621000436875; Registered 16/04/2021 https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=381476 ., (© 2024. The Author(s).)

Aberrant cell cycle progression is a hallmark of solid tumors. Therefore, cell cycle analysis is an invaluable technique to study cancer cell biology. However, cell cycle progression has been most commonly assessed by methods that are limited to temporal snapshots or that lack spatial information. In this chapter, we describe a technique that allows spatiotemporal real-time tracking of cell cycle progression of individual cells in a multicellular context. The power of this system lies in the use of 3D melanoma spheroids generated from melanoma cells engineered with the fluorescent ubiquitination-based cell cycle indicator (FUCCI). This technique, combined with mathematical modeling, allows us to gain further and more detailed insight into several relevant aspects of solid cancer cell biology, such as tumor growth, proliferation, invasion, and drug sensitivity., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Throughout the life sciences, we routinely seek to interpret measurements and observations using parametrized mechanistic mathematical models. A fundamental and often overlooked choice in this approach involves relating the solution of a mathematical model with noisy and incomplete measurement data. This is often achieved by assuming that the data are noisy measurements of the solution of a deterministic mathematical model, and that measurement errors are additive and normally distributed. While this assumption of additive Gaussian noise is extremely common and simple to implement and interpret, it is often unjustified and can lead to poor parameter estimates and non-physical predictions. One way to overcome this challenge is to implement a different measurement error model. In this review, we demonstrate how to implement a range of measurement error models in a likelihood-based framework for estimation, identifiability analysis and prediction, called profile-wise analysis. This frequentist approach to uncertainty quantification for mechanistic models leverages the profile likelihood for targeting parameters and understanding their influence on predictions. Case studies, motivated by simple caricature models routinely used in systems biology and mathematical biology literature, illustrate how the same ideas apply to different types of mathematical models. Open-source Julia code to reproduce results is available on GitHub.

Co-culture tumour spheroid experiments are routinely performed to investigate cancer progression and test anti-cancer therapies. Therefore, methods to quantitatively characterise and interpret co-culture spheroid growth are of great interest. However, co-culture spheroid growth is complex. Multiple biological processes occur on overlapping timescales and different cell types within the spheroid may have different characteristics, such as differing proliferation rates or responses to nutrient availability. At present there is no standard, widely-accepted mathematical model of such complex spatio-temporal growth processes. Typical approaches to analyse these experiments focus on the late-time temporal evolution of spheroid size and overlook early-time spheroid formation, spheroid structure and geometry. Here, using a range of ordinary differential equation-based mathematical models and parameter estimation, we interpret new co-culture experimental data. We provide new biological insights about spheroid formation, growth, and structure. As part of this analysis we connect Greenspan's seminal mathematical model to co-culture data for the first time. Furthermore, we generalise a class of compartment-based spheroid mathematical models that have previously been restricted to one population so they can be applied to multiple populations. As special cases of the general model, we explore multiple natural two population extensions to Greenspan's seminal model and reveal biological mechanisms that can describe the internal dynamics of growing co-culture spheroids and those that cannot. This mathematical and statistical modelling-based framework is well-suited to analyse spheroids grown with multiple different cell types and the new class of mathematical models provide opportunities for further mathematical and biological insights., (© 2023. The Author(s), under exclusive licence to Society for Mathematical Biology.)

Background: Proximal ulna fracture-dislocations are challenging injuries with a myriad of existing classification systems. The Coronoid, proximal Ulna, Radius, and Ligaments classification (CURL) is a simple framework designed to focus attention on the key components affecting outcome and guide surgical management. This study evaluates interobserver and intraobserver reliability of this new classification., Methods: Four observers independently reviewed plain radiographs and computed tomography (CT) scans of patients with proximal ulna fracture-dislocations. Each observer scored the Coronoid (C), proximal Ulna (U), and Radius (R) components for each fracture on 2 occasions. The osseous components were subclassified as 'intact', 'simple', or 'complex'. The Ligament component (L) was not rated as this requires intraoperative classification. Interobserver and intraobserver reliability was calculated using Cohen's weighted kappa coefficients. X-ray and CT were compared for patients with both imaging modalities. The Landis and Koch criteria were used to interpret the strength of the kappa statistics., Results: One hundred seventy seven patients had plain X-rays; 58 patients had both X-ray and CT scans. Overall, in the X-ray only cohort, there was ' almost perfect' interobserver reliability for the radial head (k = 0.94) and coronoid (k = 0.83), and ' substantial' reliability (k = 0.68) for the proximal ulna. For the X-ray and CT cohort, interobserver reliability was ' almost perfect' across both modalities for the radial head (k = 0.88 and k = 0.93, respectively) and ' moderate' for the proximal ulna (k = 0.48 and k = 0.52, respectively). For the coronoid, interobserver reliability for X-ray interpretation was ' substantial' (k = 0.74) and for CT was ' almost perfect' (k = 0.89). Intraobserver reliability was ' almost perfect' for all components, other than CT assessment of the proximal ulna which demonstrated ' substantial' reliability (k = 0.74)., Conclusion: The Coronoid, proximal Ulna, Radius, and Ligaments classification demonstrates strong interobserver and intraobserver reliability, supporting use of the classification for proximal ulna fracture-dislocations. CT is recommended for improved characterization of any fracture with a coronoid component., (© 2023 The Authors.)

Background: Microdosing psychedelic drugs is a widespread social phenomenon with diverse benefits claimed for mood and cognition. Randomized controlled trials have failed to support these claims, but the laboratory-based dosing in trials conducted to date may have limited ecological validity., Methods: Healthy male volunteers were randomized into lysergic acid diethylamide (LSD) (n = 40) and placebo (n = 40) groups and received 14 doses of either 10 μg LSD or an inactive placebo every 3 days for 6 weeks. First doses were given in a supervised laboratory setting, with other doses self-administered in a naturalistic setting. Results of safety data, blinding, daily questionnaires, expectancy, and pre-/postintervention psychometrics and cognitive tasks are presented here., Results: The most notable reported adverse event was treatment-related anxiety, which prompted the withdrawal of 4 participants from the LSD group. Daily questionnaires showed credible evidence (>99% posterior probability) of improved ratings of creativity, connectedness, energy, happiness, irritability, and wellness on dose days compared with nondose days, and these effects remained when controlling for preintervention expectancy. No questionnaire or cognitive task showed a credible change between baseline and 6-week assessment time points., Conclusions: Microdosing LSD appears to be relatively safe in healthy adult men, notwithstanding a risk of anxiety. While microdosing elicited transient increases in scales associated with mood-elevating effects, it was not sufficient to promote enduring changes to overall mood or cognition in healthy adults. Future microdosing trials in clinical populations will require the use of active placebos to control for placebo effects and dose titration to adjust for interindividual variability in drug response., (Copyright © 2023 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Climate change-related impacts have hampered the productivity of agricultural lands in recent times, affecting food security globally. Novel technology-based agricultural production systems such as controlled-environment agriculture (CEA) are a way to reduce the impact of climatic variation and pests that harm current global crop production and ensure consistent crop development. These systems often use artificial lighting and soilless mediums to produce crops. This meta-analysis has investigated the key influencing factors on crop production within these systems, using previous studies on lettuce (the most cultivated crop in these systems) to understand what affects yield within CEA. This analysis has found that on average, CEA systems yield twice that of field-based agriculture (3.68 kg m -2 vs. 1.88 kg m -2 ), with the most influencing factors being the variety of cultivars grown, the season, the nutrient delivery method, and the lighting type. The cultivation time for this study was 40 days, with 94% of papers having trial periods of 70 days or less, much lower than field-based agriculture (60-120 days). Vertical farming (stacked vertical CEA cultivation) studies were found to especially drive up yield per area (6.88 kg m -2 ). The results of this meta-analysis are useful for starting to understand the key influencing factors on CEA growth and highlight the breadth of research ongoing in the CEA industry.

Competing Interests: Competing interests: none.

Tumours are subject to external environmental variability. However, in vitro tumour spheroid experiments, used to understand cancer progression and develop cancer therapies, have been routinely performed for the past fifty years in constant external environments. Furthermore, spheroids are typically grown in ambient atmospheric oxygen (normoxia), whereas most in vivo tumours exist in hypoxic environments. Therefore, there are clear discrepancies between in vitro and in vivo conditions. We explore these discrepancies by combining tools from experimental biology, mathematical modelling, and statistical uncertainty quantification. Focusing on oxygen variability to develop our framework, we reveal key biological mechanisms governing tumour spheroid growth. Growing spheroids in time-dependent conditions, we identify and quantify novel biological adaptation mechanisms, including unexpected necrotic core removal, and transient reversal of the tumour spheroid growth phases., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Murphy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Depersonalization and derealization refer to an estranged state of mind that involves a profound feeling of detachment from one's sense of self and the surrounding environment, respectively. The phenomena co-occur on a continuum of severity, ranging from a transient experience as a normal reaction to a traumatic event to a highly debilitating condition with persistent symptoms, formally described as depersonalization/derealization disorder (DPDR). Lack of awareness of DPDR is partly due to a limited neurobiological framework, and there remains a significant risk of misdiagnosis in clinical practice. Earlier literature has focused on several brain regions involved in the experience of depersonalization and derealization, including adaptive responses to stress via defense cascades comprising autonomic functioning, the hypothalamic-pituitary-adrenal (HPA) axis, and various other neurocircuits. Recent evidence has also demonstrated the role of more complex mechanisms that are bolstered by dissociative features, such as emotional dysregulation and disintegration of the body schema. This review intends to abridge the prevailing knowledge regarding structural and functional brain alterations associated with DPDR with that of its heterogenic manifestations. DPDR is not merely the disruption of various sensory integrations, but also of several large-scale brain networks. Although a comprehensive antidote is not available for DPDR, a holistic route to the neurobiological context in DPDR may improve general understanding of the disorder and help afflicted individuals re-establish their sense of personal identity. Such information may also be useful in the development of novel pharmacological agents and targeted psychological interventions., Competing Interests: DISCLOSURES: The author has no conflicts of interest relevant to the content of this article., (Copyright © 2023. Matrix Medical Communications. All rights reserved.)

Calibrating mathematical models to describe ecological data provides important insight via parameter estimation that is not possible from analysing data alone. When we undertake a mathematical modelling study of ecological or biological data, we must deal with the trade-off between data availability and model complexity. Dealing with the nexus between data availability and model complexity is an ongoing challenge in mathematical modelling, particularly in mathematical biology and mathematical ecology where data collection is often not standardised, and more broad questions about model selection remain relatively open. Therefore, choosing an appropriate model almost always requires case-by-case consideration. In this work we present a straightforward approach to quantitatively explore this trade-off using a case study exploring mathematical models of coral reef regrowth after some ecological disturbance, such as damage caused by a tropical cyclone. In particular, we compare a simple single species ordinary differential equation (ODE) model approach with a more complicated two-species coupled ODE model. Univariate profile likelihood analysis suggests that the both models are practically identifiable. To provide additional insight we construct and compare approximate prediction intervals using a new parameter-wise prediction approximation, confirming both the simple and complex models perform similarly with regard to making predictions. Our approximate parameter-wise prediction interval analysis provides explicit information about how each parameter affects the predictions of each model. Comparing our approximate prediction intervals with a more rigorous and computationally expensive evaluation of the full likelihood shows that the new approximations are reasonable in this case. All algorithms and software to support this work are freely available as jupyter notebooks on GitHub so that they can be adapted to deal with any other ODE-based models., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Military organisations have battled communicable disease for millennia. They have pioneered disease prevention from the Crusades to the World Wars and continue to do so today. Predeployment vaccinations and chemoprophylaxis are effective in preventing communicable disease, as is reliable vector destruction and bite prevention, especially in the era of multidrug resistant organisms. These measures are unlikely to be fully possible in disasters, but reactive vaccination and efforts to reduce exposure to communicable disease should be a priority. Communicable diseases can be challenging to diagnose-the UK Defence Medical Services have become familiar with tools such as multiplex PCR and mass spectrometry. These have the potential to accurately identify organisms and sensitivity patterns in austere environments. Management of communicable diseases depends on accurate diagnosis and has a largely well-established evidence base but can be limited by a lack of resources and skills in an austere setting, therefore telemedicine can assist diagnosis and treatment of infections by projecting specialist skill. Systems such as EpiNATO2 are useful in monitoring diseases and identifying trends in order to establish control measures. Many of these tools and techniques are effective in austere environments and offer learning opportunities for those providing care in similar settings. Further research is ongoing into diagnostic tools as well as remote management., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)

Throughout the life sciences, biological populations undergo multiple phases of growth, often referred to as biphasic growth for the commonly encountered situation involving two phases. Biphasic population growth occurs over a massive range of spatial and temporal scales, ranging from microscopic growth of tumours over several days, to decades-long regrowth of corals in coral reefs that can extend for hundreds of kilometres. Different mathematical models and statistical methods are used to diagnose, understand and predict biphasic growth. Common approaches can lead to inaccurate predictions of future growth that may result in inappropriate management and intervention strategies being implemented. Here, we develop a very general computationally efficient framework, based on profile likelihood analysis, for diagnosing, understanding and predicting biphasic population growth. The two key components of the framework are as follows: (i) an efficient method to form approximate confidence intervals for the change point of the growth dynamics and model parameters and (ii) parameter-wise profile predictions that systematically reveal the influence of individual model parameters on predictions. To illustrate our framework we explore real-world case studies across the life sciences.

In vitro tumour spheroids have been used to study avascular tumour growth and drug design for over 50 years. Tumour spheroids exhibit heterogeneity within the growing population that is thought to be related to spatial and temporal differences in nutrient availability. The recent development of real-time fluorescent cell cycle imaging allows us to identify the position and cell cycle status of individual cells within the growing spheroid, giving rise to the notion of a four-dimensional (4D) tumour spheroid. We develop the first stochastic individual-based model (IBM) of a 4D tumour spheroid and show that IBM simulation data compares well with experimental data using a primary human melanoma cell line. The IBM provides quantitative information about nutrient availability within the spheroid, which is important because it is difficult to measure these data experimentally.

Tumour spheroid experiments are routinely used to study cancer progression and treatment. Various and inconsistent experimental designs are used, leading to challenges in interpretation and reproducibility. Using multiple experimental designs, live-dead cell staining, and real-time cell cycle imaging, we measure necrotic and proliferation-inhibited regions in over 1000 4D tumour spheroids (3D space plus cell cycle status). By intentionally varying the initial spheroid size and temporal sampling frequencies across multiple cell lines, we collect an abundance of measurements of internal spheroid structure. These data are difficult to compare and interpret. However, using an objective mathematical modelling framework and statistical identifiability analysis we quantitatively compare experimental designs and identify design choices that produce reliable biological insight. Measurements of internal spheroid structure provide the most insight, whereas varying initial spheroid size and temporal measurement frequency is less important. Our general framework applies to spheroids grown in different conditions and with different cell types., (© 2022. The Author(s).)