Development and characterization of curcumin nanosuspension-embedded genipin-crosslinked chitosan/polyvinylpyrrolidone hydrogel patch for effective wound healing.

This study investigated the development of a genipin-crosslinked chitosan (CS)-based polyvinylpyrrolidone (PVP) hydrogel containing curcumin nanosuspensions (Cur-NSs) for promoting wound healing in an excisional wound model. Cur-NSs were first prepared, and a simplex centroid mixture design was employed to optimize the hydrogel for high water absorption, degree of crosslinking, and sufficient toughness. The in vivo wound healing effect was investigated on Wistar rats. The optimized hydrogel consisted of CS:PVP at a 70:30 ratio, crosslinked with a 2 % w/w genipin solution. It exhibited high swelling capability (486 %) while maintaining solidity, robustness, and durability. Incorporation of 5 % w/w Cur-NSs resulted in a more compact structure, although with reduction in swelling properties. The release kinetics of Cur from the hydrogel followed the Korsmeyer-Peppas Fickian diffusion model. In vitro biocompatibility studies demonstrated that the hydrogel was non-toxic to skin fibroblast cells. The in vivo experiment revealed a desirable wound healing rate with over 80 % recovery at day 7. Cur-NSs likely aided wound healing by reducing inflammatory response and stimulating fibroblast proliferation. Additionally, the CS-based hydrogel provided a moist wound environment with hydration and gas transfer, further accelerating wound closure. These findings suggest that the Cur-NS-embedded hydrogel shows promise as a desirable wound dressing material.

Insights on the role of blocking agent on the properties of the lipase from Thermomyces lanuginosus immobilized on heterofunctional support for hydroesterification reactions.

Here, we report a study of the effect of the blocking agent on the properties of the lipase from Thermomyces lanuginosus (TLL) immobilized on a heterofunctional support (Purolite C18-ethylnediamina (EDA)- vinyl sulfone (VS)-TLL-blocking agent) in different reactions. The performance of the biocatalysts was compared to those immobilized on standard hydrophobic support (Purolite C18-TLL) and the commercial one (TLL-IM). The nature of the blocking agent (Cys, Gly and Asp) altered the enzyme features. TLL-IM always gave a comparatively worse performance, with its specificity for the oil being very different to the Purolite biocatalysts. Under optimized conditions, Purolite C18-TLL yielded 97 % of hydrolysis conversion after 4 h using a water/waste cooking soybean oil (WCSO) mass ratio of 4.3, biocatalyst load of 6.5 wt% and a temperature of 44.2 °C (without buffer or emulsification agent). In esterification reactions of the purified free fatty acids (FFAs) obtained from WCSO, the best TLL biocatalysts depended on the utilized alcohol: linear amyl alcohol was preferred by Purolite C18-TLL and Purolite C18-EDA-VS-TLL-Gly, while higher activity was achieved utilizing isoamyl alcohol as nucleophile by Purolite C18-EDA-VS-TLL-Cys, Purolite C18-EDA-VS-TLL-Asp and IM-TLL as catalysts. All the results indicate the influence of the blocking step on the final biocatalyst features.

Impact of "3414" fertilization on the yield and quality of greenhouse tomatoes.

This study aimed to explore the effects of different nitrogen, phosphorus, and potassium ratios on the yield and nutritional quality of greenhouse tomatoes under a water and fertilizer integration model. Greenhouse tomatoes were used as the research object, and the "3414" fertilizer trial design was employed to assess tomato growth, yield, quality, and soil indicators across various treatment combinations. The goal was to determine the optimal fertilization scheme and recommend appropriate fertilizer quantities for tomato cultivation and production. The results revealed that different fertilizer ratios significantly affected both the quality and yield of tomatoes. Overall, the tomato yield tended to increase with higher fertilization amounts, with potassium exhibiting the most pronounced effect on yield increase, followed by phosphorus and nitrogen. The comprehensive analysis of principal components indicated that the N2P2K1 treatment yielded the highest nutritional quality and yield. Therefore, the best fertilization combination identified in this study consisted of nitrogen fertilizer at 197.28 kg hm-2, phosphorus fertilizer at 88.75 kg hm-2, and potassium fertilizer at 229.80 kg hm-2. These findings provided the scientific basis for optimizing fertilization practices in greenhouse tomato cultivation and production in the Jilin Province.

Erratum: Effects of COVID-19-targeted non-pharmaceutical interventions on pediatric hospital admissions in North Italian hospitals, 2017 to 2022: a quasi-experimental study interrupted time-series analysis.

[This corrects the article DOI: 10.3389/fpubh.2024.1393677.].

Identifying Bayesian optimal experiments for uncertain biochemical pathway models.

Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.

Are We There Yet? Assessing the Readiness of Single-Cell Proteomics to Answer Biological Hypotheses.

Single-cell analysis is an active area of research in many fields of biology. Measurements at single-cell resolution allow researchers to study diverse populations without losing biologically meaningful information to sample averages. Many technologies have been used to study single cells, including mass spectrometry-based single-cell proteomics (SCP). SCP has seen a lot of growth over the past couple of years through improvements in data acquisition and analysis, leading to greater proteomic depth. Because method development has been the main focus in SCP, biological applications have been sprinkled in only as proof-of-concept. However, SCP methods now provide significant coverage of the proteome and have been implemented in many laboratories. Thus, a primary question to address in our community is whether the current state of technology is ready for widespread adoption for biological inquiry. In this Perspective, we examine the potential for SCP in three thematic areas of biological investigation: cell annotation, developmental trajectories, and spatial mapping. We identify that the primary limitation of SCP is sample throughput. As proteome depth has been the primary target for method development to date, we advocate for a change in focus to facilitate measuring tens of thousands of single-cell proteomes to enable biological applications beyond proof-of-concept.

The Role of CMC Statisticians: Co-Practitioners of the Scientific Method.

Chemistry, manufacturing, and control (CMC) statisticians play a key role in the development and lifecycle management of pharmaceutical and biological products, working with their non-statistician partners to manage product quality. Information used to make quality decisions comes from studies, where success is facilitated through adherence to the scientific method. This is carried out in four steps: (1) an objective, (2) design, (3) conduct, and (4) analysis. Careful consideration of each step helps to ensure that a study conclusion and associated decision is correct. This can be a development decision related to the validity of an assay or a quality decision like conformance to specifications. Importantly, all decisions are made with risk. Conventional statistical risks such as Type 1 and Type 2 errors can be coupled with associated impacts to manage patient value as well as development and commercial costs. The CMC statistician brings focus on managing risk across the steps of the scientific method, leading to optimal product development and robust supply of life saving drugs and biologicals.

Model selection of GLMMs in the analysis of count data in single-case studies: A Monte Carlo simulation.

Generalized linear mixed models (GLMMs) have great potential to deal with count data in single-case experimental designs (SCEDs). However, applied researchers have faced challenges in making various statistical decisions when using such advanced statistical techniques in their own research. This study focused on a critical issue by investigating the selection of an appropriate distribution to handle different types of count data in SCEDs due to overdispersion and/or zero-inflation. To achieve this, I proposed two model selection frameworks, one based on calculating information criteria (AIC and BIC) and another based on utilizing a multistage-model selection procedure. Four data scenarios were simulated including Poisson, negative binominal (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB). The same set of models (i.e., Poisson, NB, ZIP, and ZINB) were fitted for each scenario. In the simulation, I evaluated 10 model selection strategies within the two frameworks by assessing the model selection bias and its consequences on the accuracy of the treatment effect estimates and inferential statistics. Based on the simulation results and previous work, I provide recommendations regarding which model selection methods should be adopted in different scenarios. The implications, limitations, and future research directions are also discussed.

Exploring Anesthesia Provider Preferences for Precision Feedback: Preference Elicitation Study.

Background: Health care professionals must learn continuously as a core part of their work. As the rate of knowledge production in biomedicine increases, better support for health care professionals' continuous learning is needed. In health systems, feedback is pervasive and is widely considered to be essential for learning that drives improvement. Clinical quality dashboards are one widely deployed approach to delivering feedback, but engagement with these systems is commonly low, reflecting a limited understanding of how to improve the effectiveness of feedback about health care. When coaches and facilitators deliver feedback for improving performance, they aim to be responsive to the recipient's motivations, information needs, and preferences. However, such functionality is largely missing from dashboards and feedback reports. Precision feedback is the delivery of high-value, motivating performance information that is prioritized based on its motivational potential for a specific recipient, including their needs and preferences. Anesthesia care offers a clinical domain with high-quality performance data and an abundance of evidence-based quality metrics. Objective: The objective of this study is to explore anesthesia provider preferences for precision feedback. Methods: We developed a test set of precision feedback messages with balanced characteristics across 4 performance scenarios. We created an experimental design to expose participants to contrasting message versions. We recruited anesthesia providers and elicited their preferences through analysis of the content of preferred messages. Participants additionally rated their perceived benefit of preferred messages to clinical practice on a 5-point Likert scale. Results: We elicited preferences and feedback message benefit ratings from 35 participants. Preferences were diverse across participants but largely consistent within participants. Participants' preferences were consistent for message temporality (α=.85) and display format (α=.80). Ratings of participants' perceived benefit to clinical practice of preferred messages were high (mean rating 4.27, SD 0.77). Conclusions: Health care professionals exhibited diverse yet internally consistent preferences for precision feedback across a set of performance scenarios, while also giving messages high ratings of perceived benefit. A "one-size-fits-most approach" to performance feedback delivery would not appear to satisfy these preferences. Precision feedback systems may hold potential to improve support for health care professionals' continuous learning by accommodating feedback preferences.

Artificial intelligence for neuro MRI acquisition: a review.

OBJECT: To review recent advances of artificial intelligence (AI) in enhancing the efficiency and throughput of the MRI acquisition workflow in neuroimaging, including planning, sequence design, and correction of acquisition artifacts. MATERIALS AND METHODS: A comprehensive analysis was conducted on recent AI-based methods in neuro MRI acquisition. The study focused on key technological advances, their impact on clinical practice, and potential risks associated with these methods. RESULTS: The findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput. Specific algorithms were identified as particularly effective in optimizing acquisition steps, with reported improvements in workflow efficiency. DISCUSSION: The review highlights the transformative potential of AI in neuro MRI acquisition, emphasizing the technological advances and clinical benefits. However, it also discusses potential risks and challenges, suggesting areas for future research to mitigate these concerns and further enhance AI integration in MRI acquisition.

Do later school start times improve adolescents' sleep and substance use? A quasi-experimental study.

OBJECTIVE: A later school start time policy has been recommended as a solution to adolescents' sleep deprivation. We estimated the impacts of later school start times on adolescents' sleep and substance use by leveraging a quasi-experiment in which school start time was delayed in some regions in South Korea. METHODS: A later school start time policy was implemented in 2014 and 2015, which delayed school start times by approximately 30-90 minutes. We applied difference-in-differences and event-study designs to longitudinal data on a nationally representative cohort of adolescents from 2010 to 2015, which annually tracked sleep and substance use of 1133 adolescents from grade 7 through grade 12. RESULTS: The adoption of a later school start time policy was initially associated with a 19-minute increase in sleep duration (95% CI, 5.52 to 32.04), driven by a delayed wake time and consistent bedtime. The policy was also associated with statistically significant reductions in monthly smoking and drinking frequencies. However, approximately a year after implementation, the observed increase in sleep duration shrank to 7  minutes (95% CI, -12.60 to 25.86) and became statistically nonsignificant. Similarly, the observed reduction in smoking and drinking was attenuated a year after. CONCLUSIONS: Our findings suggest that policies that increase sleep in adolescents may have positive effects on health behaviors, but additional efforts may be required to sustain positive impacts over time. Physicians and education and health policymakers should consider the long-term effects of later school start times on adolescent health and well-being.

Factors affecting response variables with emphasis on drug release and loading for optimization of liposomes.

Drug delivery through Liposomes has shown tremendous potential in terms of the therapeutic application of nanoparticles. There are several drug-loaded liposomal formulations approved for clinical use that help mitigate harmful effects of life-threatening diseases. Developments in the field of liposomal formulations and drug delivery have made it possible for clinicians and researchers to find therapeutic solutions for complicated medical conditions. A key aspect in the development of drug-loaded liposomes is a careful review of optimization techniques to improve the overall formulation stability and efficacy. Optimization studies help in improving/modulating the various properties of drug-loaded liposomes and are vital for the development of this class of delivery systems. A comprehensive overview of the various process variables and factors involved in the optimization of drug-loaded liposomes is presented in this review. The influence of different independent variables on drug release and loading properties with the application of a statistical experimental design is also explained in this article.

Periodically, liposomes have shown tremendous potential as drug carriers as they are multifunctional nanoparticles with a unique ability to deliver drugs and other therapeutic moieties to target sites in the body. The use of statistical experimental designs and optimization models to develop drug-loaded liposomes is considered the most effective step in formulation development. A careful consideration of various factors and variables in optimizing liposome formulations has been specifically described in this review article. Thorough understanding of different factors that affect drug loading and release in liposomes provides deeper insights in achieving a stable, efficacious drug formulation. There are several new aspects and concepts which need to be explored as part of formulation development and optimization of drug-loaded liposomes and this article hopes to shed light on some important aspects in this scientific journey.

Reducing mental health stigma among nursing students through an educational intervention.

BACKGROUND: Patient-centred care must consider service consumers' needs and improve their health and well-being as well as their satisfaction and the quality of their healthcare assistance. However, individuals experiencing mental illness often struggle with barriers and misunderstandings by health care professionals. Nursing Faculties are uniquely positioned to reduce stigmatising attitudes via new educational interventions that could align nursing curricula and individuals with mental illness needs. However, there are few studies about the stigma toward mental illness among nursing students in Spain. OBJECTIVES: The present study aimed to analyse the effect of a 9-month educational intervention on the level of stigma in nursing students. DESIGN: a one-group pre-postintervention design with no control group was conducted. METHODS: A total of 194 nursing students completed the EVEPEM scale before and after the intervention. The intervention comprised 55 hours of campus sessions and 150 h practicum in mental health settings. RESULTS: A destigmatising tendency was captured by a large main effect and a statistically significant stigma reduction. CONCLUSIONS: The educational 9-month intervention was effective in reducing students' stigma by providing theoretical education, personal contact with mental health service consumers, and critical reflection activities.

The effect of an online acceptance and commitment intervention on the meaning-making process in cancer patients following hematopoietic cell transplantation: study protocol for a randomized controlled trial enhanced with single-case experimental design.

BACKGROUND: Hematopoietic cell transplantation (HCT) is a highly invasive and life-threatening treatment for hematological neoplasms and some types of cancer that can challenge the patient's meaning structures. Restoring meaning (i.e., building more flexible and significant explanations of the disease and treatment burden) can be aided by strengthening psychological flexibility by means of an Acceptance and Commitment Therapy (ACT) intervention. Thus, this trial aims to examine the effect of the ACT intervention on the meaning-making process and the underlying mechanisms of change in patients following HCT compared to a minimally enhanced usual care (mEUC) control group. The trial will be enhanced with a single-case experimental design (SCED), where ACT interventions will be compared between individuals with various pre-intervention intervals. METHODS: In total, 192 patients who qualify for the first autologous or allogeneic HCT will be recruited for a two-armed parallel randomized controlled trial comparing an online self-help 14-day ACT training to education sessions (recommendations following HCT). In both conditions, participants will receive once a day a short survey and intervention proposal (about 5-10 min a day) in the outpatient period. Double-blinded assessment will be conducted at baseline, during the intervention, immediately, 1 month, and 3 months after the intervention. In addition, 6-9 participants will be invited to SCED and randomly assigned to pre-intervention measurement length (1-3 weeks) before completing ACT intervention, followed by 7-day observations at the 2nd and 3rd post-intervention measure. The primary outcome is meaning-related distress. Secondary outcomes include psychological flexibility, meaning-making coping, meanings made, and well-being as well as global and situational meaning. DISCUSSION: This trial represents the first study that integrates the ACT and meaning-making frameworks to reduce meaning-related distress, stimulate the meaning-making process, and enhance the well-being of HCT recipients. Testing of an intervention to address existential concerns unique to patients undergoing HCT will be reinforced by a statistically rigorous idiographic approach to see what works for whom and when. Since access to interventions in the HCT population is limited, the web-based ACT self-help program could potentially fill this gap. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT06266182. Registered on February 20, 2024.

ExGenet, Integrating Design of Experiments and Response Surface Methodology for Cancer Gene Detection in Gene Regulatory Networks.

Objective: Network analysis techniques often require tuning hyperparameters for optimal performance. For instance, the independent cascade model necessitates determining the probability of diffusion. Despite its importance, a consensus on effective parameter adjustment remains elusive. Methods: In this study, we propose a novel approach utilizing experimental design methodologies, specifically 2-Factorial Analysis for Screening, and Response Surface Methodology (RSM) for parameter adjustment. We apply this methodology to the task of detecting cancer driver genes in colorectal cancer. Result: Through experimental validation of colorectal cancer data, we demonstrate the effectiveness of our proposed methodology. Compared with existing methods, our approach offers several advantages, including reduced computational overhead, systematic parameter selection grounded in statistical theory, and improved performance in detecting cancer driver genes. Conclusion: This study presents a significant advancement in the field of network analysis by providing a practical and systematic approach to hyperparameter tuning. By optimizing parameter settings, our methodology offers promising implications for critical biomedical applications such as cancer driver gene detection.

Neuromuscular electrical stimulation to combat cognitive aging in people with spinal cord injury: protocol for a single case experimental design study.

INTRODUCTION: Individuals with spinal cord injury (SCI) can experience accelerated cognitive aging. Myokines (factors released from muscle cells during contractions), such as brain-derived neurotrophic factor (BDNF), are thought to have beneficial effects on cognition. Neuromuscular electrical stimulation (NMES) was shown to elicit a large release of myokines. However, the effects of NMES on cognitive function have not been studied. OBJECTIVE: To present the study protocol for a clinical trial evaluating the effects of NMES aimed at improving cognition and BDNF. METHODS: A replicated randomized three-phases single-case experimental design (SCED) with sequential multiple baseline time series and a single-armed prospective trial will be conducted with 15 adults with chronic SCI (> 12 months after injury) above L1 neurological level undergoing 30-min quadriceps NMES, 3 days per week for 12 weeks. MAIN STUDY ENDPOINTS: Primary endpoint is cognitive performance (assessed by a smartphone test) conducted three times per week during the baseline phase with random duration of 3 to 8 weeks, the intervention phase of 12 weeks, and the follow-up phase of 3 weeks after a no measurement rest period of 12 weeks. Secondary endpoints are changes in BDNF levels and cognitive performance measured before the baseline period, before and after intervention and after a 12 weeks follow-up. CONCLUSION: This will be the first study investigating the effects of 12 weeks NMES on both cognition and BDNF levels in individuals with SCI. The SCED results provide information on individual treatment effect courses which may direct future research. TRIAL REGISTRATION: ClinicalTrials.gov (NCT05822297, 12/01/2023).

A Methodology for Robust Multislice Ptychography.

While multislice electron ptychography can provide thermal vibration limited resolution and structural information in 3D, it relies on properly selecting many intertwined acquisition and computational parameters. Here, we outline a methodology for selecting acquisition parameters to enable robust ptychographic reconstructions. We develop two physically informed metrics, areal oversampling and Ronchigram magnification, to describe the selection of these parameters in multislice ptychography. Through simulations, we comprehensively evaluate the validity of these two metrics over a broad range of conditions and show that they accurately guide reconstruction success. Further, we validate these conclusions with experimental ptychographic data and demonstrate close agreement between trends in simulated and experimental data. Using these metrics, we achieve experimental multislice reconstructions at a scan step of 2.1Å/px, enabling large field-of-view, data-efficient reconstructions. These experimental design principles enable the routine and robust use of multislice ptychography for 3D characterization of materials at the atomic scale.

Analytical quality by design-based development of a capillary electrophoresis method for Omeprazole impurity profiling.

Omeprazole (OME) is a proton pump inhibitor used to treat gastroesophageal reflux disease associated conditions. The current study presents an Analytical Quality by Design-based approach for the development of a CE method for OME impurity profiling. The scouting experiments suggested the selection of solvent modified Micellar ElectroKinetic Chromatography operative mode using a pseudostationary phase composed of sodium dodecyl sulfate (SDS) micelles and n-butanol as organic modifier in borate buffer. A symmetric three-level screening matrix 37//16 was used to evaluate the effect of Critical Method Parameters, including Background Electrolyte composition and instrumental settings, on Critical Method Attributes (critical resolution values, OME peak width and analysis time). The analytical procedure was optimized using Response Surface Methodology through a Central Composite Orthogonal Design. Risk of failure maps made it possible to define the Method Operable Design Region, within which the following optimized conditions were selected: 72 mM borate buffer pH 10.0, 96 mM SDS, 1.45 %v/v n-butanol, capillary temperature 21 °C, applied voltage 25 kV. The method was validated according to ICH guidelines and robustness was evaluated using a Plackett-Burman design. The developed procedure enables the simultaneous determination of OME and seven related impurities, and has been successfully applied to the analysis of pharmaceutical formulations.

Systematic review for optimizing sample size in dairy cow methane emission studies in temperate regions: a comprehensive methodological approach.

This research introduces a systematic framework for calculating sample size in studies focusing on enteric methane (CH4, g/kg of DMI) yield reduction in dairy cows. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search across the Web of Science, Scopus, and PubMed Central databases for studies published from 2012 to 2023. The inclusion criteria were: studies reporting CH4 yield and its variability in dairy cows, employing specific experimental designs (Latin Square Design (LSD), Crossover Design, Randomized Complete Block Design (RCBD), and Repeated Measures Design) and measurement methods (Open-circuit respirometry chambers (RC), the GreenFeed system, and the sulfur hexafluoride tracer technique), conducted in Canada, the United States and Europe. A total of 150 studies, which included 177 reports, met our criteria and were included in the database. Our methodology for using the database for sample size calculations began by defining 6 CH4 yield reduction levels (5, 10, 15, 20, 30, and 50%). Utilizing an adjusted Cohen's f formula and a power analysis we calculated the sample sizes required for these reductions in balanced LSD and RCBD reports from studies involving 3 or 4 treatments. The results indicate that within-subject studies (i.e., LSD) require smaller sample sizes to detect CH4 yield reductions compared with between-subject studies (i.e., RCBD). Although experiments using RC typically require fewer individuals due to their higher accuracy, our results demonstrate that this expected advantage is not evident in reports from RCBD studies with 4 treatments. A key innovation of this research is the development of a web-based tool that simplifies the process of sample size calculation (samplesizecalculator.ucdavis.edu). Developed using Python, this tool leverages the extensive database to provide tailored sample size recommendations for specific experimental scenarios. It ensures that experiments are adequately powered to detect meaningful differences in CH4 emissions, thereby contributing to the scientific rigor of studies in this critical area of environmental and agricultural research. With its user-friendly interface and robust backend calculations, this tool represents a significant advancement in the methodology for planning and executing CH4 emission studies in dairy cows, aligning with global efforts toward sustainable agricultural practices and environmental conservation.

A Systematic Quality Review of Single-Case Experimental Designs Using Acceptance and Commitment Therapy in Adult Clinical Populations.

Recent publications within Contextual Behavioral Science provided a rationale for the expansion of intervention efficacy research using methods that capture idiographic factors and processes. We conducted a systematic review of the use and quality of single-case experimental designs (SCED) within the Acceptance and Commitment Therapy (ACT) literature in adult clinical populations. The systematic review was conducted according to PRISMA guidelines and the databases CINAHL, MEDLINE, PsycINFO, PsycArticles and OpenGrey were searched for peer-reviewed articles. Further studies were sought through review of reference lists of all full text studies. Studies were assessed against What Works Clearinghouse (WWC) single-case design standards. Twenty-six studies met eligibility criteria and were conducted within research teams all implementing multiple-baseline designs. Twenty-four studies did not meet WWC standards with most failing to ensure a degree of concurrence across participants. The extent of randomisation methods was also captured. The review highlights the sparsity of SCEDs within ACT literature in clinical populations and current methodological practices. Limitations of the review and implications for future research are discussed.