Functional diversity for body actions in the mesencephalic locomotor region.

The mesencephalic locomotor region (MLR) is a key midbrain center with roles in locomotion. Despite extensive studies and clinical trials aimed at therapy-resistant Parkinson's disease (PD), debate on its function remains. Here, we reveal the existence of functionally diverse neuronal populations with distinct roles in control of body movements. We identify two spatially intermingled glutamatergic populations separable by axonal projections, mouse genetics, neuronal activity profiles, and motor functions. Most spinally projecting MLR neurons encoded the full-body behavior rearing. Loss- and gain-of-function optogenetic perturbation experiments establish a function for these neurons in controlling body extension. In contrast, Rbp4-transgene-positive MLR neurons project in an ascending direction to basal ganglia, preferentially encode the forelimb behaviors handling and grooming, and exhibit a role in modulating movement. Thus, the MLR contains glutamatergic neuronal subpopulations stratified by projection target exhibiting roles in action control not restricted to locomotion.

Type III-A CRISPR-Cas Csm Complexes: Assembly, Periodic RNA Cleavage, DNase Activity Regulation, and Autoimmunity.

Type ΙΙΙ CRISPR-Cas systems provide robust immunity against foreign RNA and DNA by sequence-specific RNase and target RNA-activated sequence-nonspecific DNase and RNase activities. We report on cryo-EM structures of Thermococcus onnurineus CsmcrRNA binary, CsmcrRNA-target RNA and CsmcrRNA-target RNAanti-tag ternary complexes in the 3.1 Å range. The topological features of the crRNA 5'-repeat tag explains the 5'-ruler mechanism for defining target cleavage sites, with accessibility of positions -2 to -5 within the 5'-repeat serving as sensors for avoidance of autoimmunity. The Csm3 thumb elements introduce periodic kinks in the crRNA-target RNA duplex, facilitating cleavage of the target RNA with 6-nt periodicity. Key Glu residues within a Csm1 loop segment of CsmcrRNA adopt a proposed autoinhibitory conformation suggestive of DNase activity regulation. These structural findings, complemented by mutational studies of key intermolecular contacts, provide insights into CsmcrRNA complex assembly, mechanisms underlying RNA targeting and site-specific periodic cleavage, regulation of DNase cleavage activity, and autoimmunity suppression.

Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common control subjects.

Genome-wide association studies (GWASs) have been performed to identify host genetic factors for a range of phenotypes, including for infectious diseases. The use of population-based common control subjects from biobanks and extensive consortia is a valuable resource to increase sample sizes in the identification of associated loci with minimal additional expense. Non-differential misclassification of the outcome has been reported when the control subjects are not well characterized, which often attenuates the true effect size. However, for infectious diseases the comparison of affected subjects to population-based common control subjects regardless of pathogen exposure can also result in selection bias. Through simulated comparisons of pathogen-exposed cases and population-based common control subjects, we demonstrate that not accounting for pathogen exposure can result in biased effect estimates and spurious genome-wide significant signals. Further, the observed association can be distorted depending upon strength of the association between a locus and pathogen exposure and the prevalence of pathogen exposure. We also used a real data example from the hepatitis C virus (HCV) genetic consortium comparing HCV spontaneous clearance to persistent infection with both well-characterized control subjects and population-based common control subjects from the UK Biobank. We find biased effect estimates for known HCV clearance-associated loci and potentially spurious HCV clearance associations. These findings suggest that the choice of control subjects is especially important for infectious diseases or outcomes that are conditional upon environmental exposures.

A single workflow for multi-species blood transcriptomics.

BACKGROUND: Blood transcriptomic analysis is widely used to provide a detailed picture of a physiological state with potential outcomes for applications in diagnostics and monitoring of the immune response to vaccines. However, multi-species transcriptomic analysis is still a challenge from a technological point of view and a standardized workflow is urgently needed to allow interspecies comparisons. RESULTS: Here, we propose a single and complete total RNA-Seq workflow to generate reliable transcriptomic data from blood samples from humans and from animals typically used in preclinical models. Blood samples from a maximum of six individuals and four different species (rabbit, non-human primate, mouse and human) were extracted and sequenced in triplicates. The workflow was evaluated using different wet-lab and dry-lab criteria, including RNA quality and quantity, the library molarity, the number of raw sequencing reads, the Phred-score quality, the GC content, the performance of ribosomal-RNA and globin depletion, the presence of residual DNA, the strandness, the percentage of coding genes, the number of genes expressed, and the presence of saturation plateau in rarefaction curves. We identified key criteria and their associated thresholds to be achieved for validating the transcriptomic workflow. In this study, we also generated an automated analysis of the transcriptomic data that streamlines the validation of the dataset generated. CONCLUSIONS: Our study has developed an end-to-end workflow that should improve the standardization and the inter-species comparison in blood transcriptomics studies. In the context of vaccines and drug development, RNA sequencing data from preclinical models can be directly compared with clinical data and used to identify potential biomarkers of value to monitor safety and efficacy.

Statistical approaches for the integration of external controls in a cystic fibrosis clinical trial: a simulation and an application.

Development of new therapeutics for a rare disease such as cystic fibrosis (CF) is hindered by challenges in accruing enough patients for clinical trials. Using external controls from well-matched historical trials can reduce prospective trial sizes, and this approach has supported regulatory approval of new interventions for other rare diseases. We consider three statistical methods that incorporate external controls into a hypothetical clinical trial of a new treatment to reduce pulmonary exacerbations in CF patients: 1) inverse probability weighting, 2) Bayesian modeling with propensity score-based power priors, and 3) hierarchical Bayesian modeling with commensurate priors. We compare the methods via simulation study and in a real clinical trial data setting. Simulations showed that bias in the treatment effect was <4% using any of the methods, with type 1 error (or in the Bayesian cases, posterior probability of the null hypothesis) usually <5%. Inverse probability weighting was sensitive to similarity in prevalence of the covariates between historical and prospective trial populations. The commensurate prior method performed best with real clinical trial data. Using external controls to reduce trial size in future clinical trials holds promise and can advance the therapeutic pipeline for rare diseases.

Toward a clearer understanding of what works to reduce gun violence: the role of falsification strategies.

Strong epidemiologic evidence from ecological and individual-level studies in the United States supports the claim that access to firearms substantially increases the risk of dying by suicide, homicide, and firearm accidents. Less certain is how well particular interventions work to prevent these deaths and other firearm-related harms. Given the limits of existing data to study firearm violence and the infeasibility of conducting randomized trials of firearm access, it is important to do the best we can with the data we already have. We argue that falsification strategies are a critical-yet underutilized-component of any such analytical approach. The falsification strategies we focus on are versions of "negative controls" analyses in which we expect that an analysis should yield a null causal effect, and thus where not obtaining a null effect estimate raises questions about the assumptions underlying causal interpretation of a study's findings. We illustrate the saliency of this issue today with examples drawn from studies published in leading peer-reviewed journals within the last 5 years. Collecting rich, high-quality data always takes time, urgent as the need may be. On the other hand, doing better with the data we already have can start right now.

Applying two approaches to detect unmeasured confounding due to time-varying variables in a self-controlled risk interval design evaluating COVID-19 vaccine safety signals, using myocarditis as a case example.

We test the robustness of the self-controlled risk interval (SCRI) design in a setting where time between doses may introduce time-varying confounding, using both negative control outcomes (NCOs) and quantitative bias analysis (QBA). All vaccinated cases identified from 5 European databases between 1 September 2020 and end of data availability were included. Exposures were doses 1-3 of the Pfizer, Moderna, AstraZeneca, and Janssen COVID-19 vaccines; outcomes were myocarditis and otitis externa (NCO). The SCRI used a 60-day control window and dose-specific 28-day risk windows, stratified by vaccine brand and adjusted for calendar time. The QBA included two scenarios: (i) baseline probability of the confounder was higher in the control window and (ii) vice versa. The NCO was not associated with any of the COVID-19 vaccine types or doses except Moderna dose 1 (IRR = 1.09, 95%CI 1.01-1.09). The QBA suggested even the strongest literature-reported confounder (COVID-19; RRmyocarditis = 18.3) could only explain away part of the observed effect from IRR = 3 to IRR = 1.40. The SCRI seems robust to unmeasured confounding in the COVID-19 setting, although a strong unmeasured confounder could bias the observed effect upward. Replication of our findings for other safety signals would strengthen this conclusion.

Applying propensity methods to the United States transplant registry for external real-world evidence control arms for 5-year survival in the BENEFIT study.

To address the challenges of assessing the impact of a reasonably likely surrogate endpoint on long-term graft survival in prospective kidney transplant clinical trials, the Transplant Therapeutics Consortium established a real-world evidence workgroup evaluating the scientific value of using transplant registry data as an external control to supplement the internal control group. The United Network for Organ Sharing retrospectively simulated the use of several distinct contemporaneous external control groups, applied multiple cause inference methods, and compared treatment effects to those observed in the BENEFIT study. Applying BENEFIT study enrollment criteria produced a smaller historical cyclosporine control arm (n = 153) and a larger, alternative (tacrolimus) historical control arm (n = 1069). Following covariate-balanced propensity scoring, Kaplan-Meier 5-year all-cause graft survivals were 81.3% and 81.7% in the Organ Procurement and Transplantation Network (OPTN) tacrolimus and cyclosporine external control arms, similar to 80.3% observed in the BENEFIT cyclosporine treatment arm. Five-year graft survival in the belatacept-less intensive arm was significantly higher than the OPTN controls using propensity scoring for comparing cyclosporine and tacrolimus. Propensity weighting using OPTN controls closely mirrored the BENEFIT study's long-term control (cyclosporine) arm's survival rate and the less intensive arm's treatment effect (significantly higher survival vs control). This study supports the feasibility and validity of using supplemental external registry controls for long-term survival in kidney transplant clinical trials.

Summix: A method for detecting and adjusting for population structure in genetic summary data.

Publicly available genetic summary data have high utility in research and the clinic, including prioritizing putative causal variants, polygenic scoring, and leveraging common controls. However, summarizing individual-level data can mask population structure, resulting in confounding, reduced power, and incorrect prioritization of putative causal variants. This limits the utility of publicly available data, especially for understudied or admixed populations where additional research and resources are most needed. Although several methods exist to estimate ancestry in individual-level data, methods to estimate ancestry proportions in summary data are lacking. Here, we present Summix, a method to efficiently deconvolute ancestry and provide ancestry-adjusted allele frequencies (AFs) from summary data. Using continental reference ancestry, African (AFR), non-Finnish European (EUR), East Asian (EAS), Indigenous American (IAM), South Asian (SAS), we obtain accurate and precise estimates (within 0.1%) for all simulation scenarios. We apply Summix to gnomAD v.2.1 exome and genome groups and subgroups, finding heterogeneous continental ancestry for several groups, including African/African American (∼84% AFR, ∼14% EUR) and American/Latinx (∼4% AFR, ∼5% EAS, ∼43% EUR, ∼46% IAM). Compared to the unadjusted gnomAD AFs, Summix's ancestry-adjusted AFs more closely match respective African and Latinx reference samples. Even on modern, dense panels of summary statistics, Summix yields results in seconds, allowing for estimation of confidence intervals via block bootstrap. Given an accompanying R package, Summix increases the utility and equity of public genetic resources, empowering novel research opportunities.

High-Performance Bifunctional Electrocatalysts for Flexible and Rechargeable Zn-Air Batteries: Recent Advances.

Flexible rechargeable Zn-air batteries (FZABs) exhibit high energy density, ultra-thin, lightweight, green, and safe features, and are considered as one of the ideal power sources for flexible wearable electronics. However, the slow and high overpotential oxygen reaction at the air cathode has become one of the key factors restricting the development of FZABs. The improvement of activity and stability of bifunctional catalysts has become a top priority. At the same time, FZABs should maintain the battery performance under different bending and twisting conditions, and the design of the overall structure of FZABs is also important. Based on the understanding of the three typical configurations and working principles of FZABs, this work highlights two common strategies for applying bifunctional catalysts to FZABs: 1) powder-based flexible air cathode and 2) flexible self-supported air cathode. It summarizes the recent advances in bifunctional oxygen electrocatalysts and explores the various types of catalyst structures as well as the related mechanistic understanding. Based on the latest catalyst research advances, this paper introduces and discusses various structure modulation strategies and expects to guide the synthesis and preparation of efficient bifunctional catalysts. Finally, the current status and challenges of bifunctional catalyst research in FZABs are summarized.

Rare variant association tests for ancestry-matched case-control data based on conditional logistic regression.

With the increasing volume of human sequencing data available, analysis incorporating external controls becomes a popular and cost-effective approach to boost statistical power in disease association studies. To prevent spurious association due to population stratification, it is important to match the ancestry backgrounds of cases and controls. However, rare variant association tests based on a standard logistic regression model are conservative when all ancestry-matched strata have the same case-control ratio and might become anti-conservative when case-control ratio varies across strata. Under the conditional logistic regression (CLR) model, we propose a weighted burden test (CLR-Burden), a variance component test (CLR-SKAT) and a hybrid test (CLR-MiST). We show that the CLR model coupled with ancestry matching is a general approach to control for population stratification, regardless of the spatial distribution of disease risks. Through extensive simulation studies, we demonstrate that the CLR-based tests robustly control type 1 errors under different matching schemes and are more powerful than the standard Burden, SKAT and MiST tests. Furthermore, because CLR-based tests allow for different case-control ratios across strata, a full-matching scheme can be employed to efficiently utilize all available cases and controls to accelerate the discovery of disease associated genes.

Recent advances in mass spectrometry for the detection of doping.

INTRODUCTION: The analysis of doping control samples is preferably performed by mass spectrometry, because obtained results meet the highest analytical standards and ensure an impressive degree of reliability. The advancement in mass spectrometry and all its associated technologies thus allow for continuous improvements in doping control analysis. AREAS COVERED: Modern mass spectrometric systems have reached a status of increased sensitivity, robustness, and specificity within the last decade. The improved sensitivity in particular has, on the other hand, also led to the detection of drug residues that were attributable to scenarios where the prohibited substances were not administered consciously but rather by the unconscious ingestion of or exposure to contaminated products. These scenarios and their doubtless clarification represent a great challenge. Here, too, modern MS systems and their applications can provide good insights in the interpretation of dose-related metabolism of prohibited substances. In addition to the development of new instruments itself, software-assisted analysis of the sometimes highly complex data is playing an increasingly important role and facilitating the work of doping control laboratories. EXPERT OPINION: The sensitive analysis and evaluation of a higher number of samples in a shorter time is made possible by the ongoing developments in mass spectrometry.

Fluorochrome-dependent specific changes in spectral profiles using different compensation beads or primary cells in full spectrum cytometry.

Full spectrum flow cytometry is a powerful tool for immune monitoring on a single-cell level and with currently available machines, panels of 40 or more markers per sample are possible. However, with an increased panel size, spectral unmixing issues arise, and appropriate single stain reference controls are required for accurate experimental results and to avoid unmixing errors. In contrast to conventional flow cytometry, full spectrum flow cytometry takes into account even minor differences in spectral signatures and requires the full spectrum of each fluorochrome to be identical in the reference control and the fully stained sample to ensure accurate and reliable results. In general, using the cells of interest is considered optimal, but certain markers may not be expressed at sufficient levels to generate a reliable positive control. In this case, compensation beads show some significant advantages as they bind a consistent amount of antibody independent of its specificity. In this study, we evaluated two types of manufactured compensation beads for use as reference controls for 30 of the most commonly used and commercially available fluorochromes in full spectrum cytometry and compared them to human and murine primary leukocytes. While most fluorochromes show the same spectral profile on beads and cells, we demonstrate that specific fluorochromes show a significantly different spectral profile depending on which type of compensation beads is used, and some fluorochromes should be used on cells exclusively. Here, we provide a list of important considerations when selecting optimal reference controls for full spectrum flow cytometry.

Executive Function in Preschool Children with Congenital Heart Disease and Controls: The Role of a Cognitively Stimulating Home Environment.

OBJECTIVE: To assess the relationships between (1) environmental and demographic factors and executive function (EF) in preschool children with congenital heart disease (CHD) and controls and (2) clinical and surgical risk factors and EF in preschool children with CHD. STUDY DESIGN: At 4-6 years of age, parents of children with CHD (n = 51) and controls (n = 124) completed the Behavior Rating Inventory of Executive Function, Preschool Version questionnaire and the Cognitively Stimulating Parenting Scale (CSPS). Multivariable general linear modeling assessed the relationship between Behavior Rating Inventory of Executive Function, Preschool Version composite scores (Inhibitory Self-Control Index [ISCI], Flexibility Index [FI], and Emergent Metacognition Index [EMI]) and group (CHD/control), sex, age at assessment, gestational age, Index of Multiple Deprivation, and CSPS scores. The relationships between CHD type, surgical factors, and brain magnetic resonance imaging injury rating and ISCI, FI, and EMI scores were assessed. RESULTS: The presence of CHD, age at assessment, sex, and Index of Multiple Deprivation were not associated with EF scores. Lower gestational age was associated with greater ISCI and FI scores, and age at assessment was associated with lower FI scores. Group significantly moderated the relationship between CSPS and EF, such that CSPS significantly predicted EF in children with CHD (ISCI: P = .0004; FI: P = .0015; EMI: P = .0004) but not controls (ISCI: P = .2727; FI: P = .6185; EMI: P = .3332). There were no significant relationships between EF scores and surgical factors, CHD type, or brain magnetic resonance imaging injury rating. CONCLUSIONS: Supporting parents to provide a cognitively stimulating home environment may improve EF in children with CHD. The home and parenting environment should be considered when designing intervention studies aimed at improving EF in this patient group.

The prevalence of controls in phyllosphere microbiome research: a methodological review.

DNA contamination can critically confound microbiome studies. Here, we take a systematic approach to review the current literature and investigate the prevalence of contamination controls in phyllosphere microbiome research over the past decade. By utilising systematic review principles for this review, we were able to conduct a thorough investigation, screening 450 articles from three databases for eligibility and extracting data in a controlled and methodical manner. Worryingly, we observed a surprisingly low usage of both positive and negative contamination controls in phyllosphere research. As a result, we propose a set of minimum standards to combat the effects of contamination in future phyllosphere research.

Exploring the role of biotic factors in regulating the spatial variability in land surface phenology across four temperate forest sites.

Land surface phenology (LSP), the characterization of plant phenology with satellite data, is essential for understanding the effects of climate change on ecosystem functions. Considerable LSP variation is observed within local landscapes, and the role of biotic factors in regulating such variation remains underexplored. In this study, we selected four National Ecological Observatory Network terrestrial sites with minor topographic relief to investigate how biotic factors regulate intra-site LSP variability. We utilized plant functional type (PFT) maps, functional traits, and LSP data to assess the explanatory power of biotic factors for the start and end of season (SOS and EOS) variability. Our results indicate that PFTs alone explain only 0.8-23.4% of intra-site SOS and EOS variation, whereas including functional traits significantly improves explanatory power, with cross-validation correlations ranging from 0.50 to 0.85. While functional traits exhibited diverse effects on SOS and EOS across different sites, traits related to competitive ability and productivity were important for explaining both SOS and EOS variation at these sites. These findings reveal that plants exhibit diverse phenological responses to comparable environmental conditions, and functional traits significantly contribute to intra-site LSP variability, highlighting the importance of intrinsic biotic properties in regulating plant phenology.

Distinct, direct and climate-mediated environmental controls on global particulate and mineral-associated organic carbon storage.

Identifying controls on soil organic carbon (SOC) storage, and where SOC is most vulnerable to loss, are essential to managing soils for both climate change mitigation and global food security. However, we currently lack a comprehensive understanding of the global drivers of SOC storage, especially with regards to particulate (POC) and mineral-associated organic carbon (MAOC). To better understand hierarchical controls on POC and MAOC, we applied path analyses to SOC fractions, climate (i.e., mean annual temperature [MAT] and mean annual precipitation minus potential evapotranspiration [MAP-PET]), carbon (C) input (i.e., net primary production [NPP]), and soil property data synthesized from 72 published studies, along with data we generated from the National Ecological Observatory Network soil pits (n = 901 total observations). To assess the utility of investigating POC and MAOC separately in understanding SOC storage controls, we then compared these results with another path analysis predicting bulk SOC storage. We found that POC storage is negatively related to MAT and soil pH, while MAOC storage is positively related to NPP and MAP-PET, but negatively related to soil % sand. Our path analysis predicting bulk SOC revealed similar trends but explained less variation in C storage than our POC and MAOC analyses. Given that temperature and pH impose constraints on microbial decomposition, this indicates that POC is primarily controlled by SOC loss processes. In contrast, strong relationships with variables related to plant productivity constraints, moisture, and mineral surface availability for sorption indicate that MAOC is primarily controlled by climate-driven variations in C inputs to the soil, as well as C stabilization mechanisms. Altogether, these results demonstrate that global POC and MAOC storage are controlled by separate environmental variables, further justifying the need to quantify and model these C fractions separately to assess and forecast the responses of SOC storage to global change.

Assessing social cognition in patients with schizophrenia and healthy controls using the reading the mind in the eyes test (RMET): a systematic review and meta-regression.

The reading the mind in the eyes test (RMET) - which assesses the theory of mind component of social cognition - is often used to compare social cognition between patients with schizophrenia and healthy controls. There is, however, no systematic review integrating the results of these studies. We identified 198 studies published before July 2020 that administered RMET to patients with schizophrenia or healthy controls from three English-language and two Chinese-language databases. These studies included 41 separate samples of patients with schizophrenia (total n = 1836) and 197 separate samples of healthy controls (total n = 23 675). The pooled RMET score was 19.76 (95% CI 18.91-20.60) in patients and 25.53 (95% CI 25.19-25.87) in controls (z = 12.41, p < 0.001). After excluding small-sample outlier studies, this difference in RMET performance was greater in studies using non-English v. English versions of RMET (Chi [Q] = 8.54, p < 0.001). Meta-regression analyses found a negative association of age with RMET score and a positive association of years of schooling with RMET score in both patients and controls. A secondary meta-analysis using a spline construction of 180 healthy control samples identified a non-monotonic relationship between age and RMET score - RMET scores increased with age before 31 and decreased with age after 31. These results indicate that patients with schizophrenia have substantial deficits in theory of mind compared with healthy controls, supporting the construct validity of RMET as a measure of social cognition. The different results for English versus non-English versions of RMET and the non-monotonic relationship between age and RMET score highlight the importance of the language of administration of RMET and the possibility that the relationship of aging with theory of mind is different from the relationship of aging with other types of cognitive functioning.

Single proxy control.

Negative control variables are sometimes used in nonexperimental studies to detect the presence of confounding by hidden factors. A negative control outcome (NCO) is an outcome that is influenced by unobserved confounders of the exposure effects on the outcome in view, but is not causally impacted by the exposure. Tchetgen Tchetgen (2013) introduced the Control Outcome Calibration Approach (COCA) as a formal NCO counterfactual method to detect and correct for residual confounding bias. For identification, COCA treats the NCO as an error-prone proxy of the treatment-free counterfactual outcome of interest, and involves regressing the NCO on the treatment-free counterfactual, together with a rank-preserving structural model, which assumes a constant individual-level causal effect. In this work, we establish nonparametric COCA identification for the average causal effect for the treated, without requiring rank-preservation, therefore accommodating unrestricted effect heterogeneity across units. This nonparametric identification result has important practical implications, as it provides single-proxy confounding control, in contrast to recently proposed proximal causal inference, which relies for identification on a pair of confounding proxies. For COCA estimation we propose 3 separate strategies: (i) an extended propensity score approach, (ii) an outcome bridge function approach, and (iii) a doubly-robust approach. Finally, we illustrate the proposed methods in an application evaluating the causal impact of a Zika virus outbreak on birth rate in Brazil.

Analytical Methods for Comparing Uncontrolled Trials with External Controls from Real-World Data: a Systematic Literature Review and Comparison to European Regulatory and Health Technology Assessment Practice.

OBJECTIVES: To provide an overview of analytical methods in scientific literature for comparing uncontrolled medicine trials to external controls from individual patient-level real-world data (IPD-RWD). In addition, to compare these methods with recommendations made in guidelines from European regulatory and health technology assessment (HTA) organizations and with their evaluations described in assessment reports. METHODS: A systematic literature review (until March 1st 2023) in PubMed and Connected Papers was performed to identify analytical methods for comparing uncontrolled trials with external controls from IPD-RWD. These methods were compared descriptively to methods recommended in method guidelines and encountered in assessment reports of the European Medicines Agency (2015-2020) and four European HTA organizations (2015-2023). RESULTS: Thirty-four identified scientific articles described analytical methods for comparing uncontrolled trial data to IPD-RWD-based external controls. The various methods covered controlling for confounding and/or dependent censoring, correction for missing data; and analytical comparative modelling methods. Seven guidelines also focused on research design, RWD quality and transparency aspects, and four of those recommended analytical methods for comparisons with IPD-RWD. The methods discussed in regulatory (n=15) and HTA (n=35) assessment reports were often based on aggregate data and lacked transparency due to the few details provided. CONCLUSION: Literature and guidelines suggest a methodological approach to comparing uncontrolled trials with external controls from IPD-RWD similar to target trial emulation, using state-of-the-art methods. External controls supporting regulatory and HTA decision-making were rarely in line with this approach. Twelve recommendations are proposed to improve the quality and acceptability of these methods.