Information Integration and Energy Expenditure in Gene Regulation.

The quantitative concepts used to reason about gene regulation largely derive from bacterial studies. We show that this bacterial paradigm cannot explain the sharp expression of a canonical developmental gene in response to a regulating transcription factor (TF). In the absence of energy expenditure, with regulatory DNA at thermodynamic equilibrium, information integration across multiple TF binding sites can generate the required sharpness, but with strong constraints on the resultant "higher-order cooperativities." Even with such integration, there is a "Hopfield barrier" to sharpness; for n TF binding sites, this barrier is represented by the Hill function with the Hill coefficient n. If, however, energy is expended to maintain regulatory DNA away from thermodynamic equilibrium, as in kinetic proofreading, this barrier can be breached and greater sharpness achieved. Our approach is grounded in fundamental physics, leads to testable experimental predictions, and suggests how a quantitative paradigm for eukaryotic gene regulation can be formulated.

Discovery of a structural class of antibiotics with explainable deep learning.

The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1-9. Deep learning approaches have aided in exploring chemical spaces1,10-15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.

Reactive metabolic byproducts contribute to antibiotic lethality under anaerobic conditions.

Understanding how bactericidal antibiotics kill bacteria remains an open question. Previous work has proposed that primary drug-target corruption leads to increased energetic demands, resulting in the generation of reactive metabolic byproducts (RMBs), particularly reactive oxygen species, that contribute to antibiotic-induced cell death. Studies have challenged this hypothesis by pointing to antibiotic lethality under anaerobic conditions. Here, we show that treatment of Escherichia coli with bactericidal antibiotics under anaerobic conditions leads to changes in the intracellular concentrations of central carbon metabolites, as well as the production of RMBs, particularly reactive electrophilic species (RES). We show that antibiotic treatment results in DNA double-strand breaks and membrane damage and demonstrate that antibiotic lethality under anaerobic conditions can be decreased by RMB scavengers, which reduce RES accumulation and mitigate associated macromolecular damage. This work indicates that RMBs, generated in response to antibiotic-induced energetic demands, contribute in part to antibiotic lethality under anaerobic conditions.

Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery.

Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein-ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning-based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true-positive rate to false-positive rate. This work indicates that advances in modeling protein-ligand interactions, particularly using machine learning-based approaches, are needed to better harness AlphaFold2 for drug discovery.

Evidence that coronavirus superspreading is fat-tailed.

Superspreaders, infected individuals who result in an outsized number of secondary cases, are believed to underlie a significant fraction of total SARS-CoV-2 transmission. Here, we combine empirical observations of SARS-CoV and SARS-CoV-2 transmission and extreme value statistics to show that the distribution of secondary cases is consistent with being fat-tailed, implying that large superspreading events are extremal, yet probable, occurrences. We integrate these results with interaction-based network models of disease transmission and show that superspreading, when it is fat-tailed, leads to pronounced transmission by increasing dispersion. Our findings indicate that large superspreading events should be the targets of interventions that minimize tail exposure.

Chinese normotensive and essential hypertensive reference intervals for plasma aldosterone and renin activity by liquid chromatography-tandem mass spectrometry.

OBJECTIVES: The renin-angiotensin-aldosterone system (RAAS) regulates blood pressure. Plasma renin activities (PRA) and plasma aldosterone concentrations (PAC) are biomarkers related to RAAS. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements for PRA and PAC have become popular. Method-specific reference intervals (RIs) are required. METHODS: Routine PRA and PAC services in a Hong Kong teaching hospital were based on LC-MS/MS methods. PRA and PAC RIs were developed for normotensive subjects and essential hypertensive (EH) patients. Healthy volunteers were recruited to establish normotensive RIs. PRA and PAC results of hypertensive patients with urine aldosterone tests for primary aldosteronism (PA) screening were retrieved from the laboratory information system. Patients without PA were included. Patients with secondary hypertension and patients on medications affecting the RAAS were excluded. The central 95% RIs were established based on the recommendations of the Clinical and Laboratory Standards Institute guideline C28-A3. RESULTS: PRA and PAC of 170 normotensive volunteers and 362 EH patients were analysed. There was no sex-specific difference in PRA and PAC for normotensive and EH reference subjects. Differences for PRA and PAC were noted between normotensive subjects aged below 45 and their older counterparts. However, such a difference was only identified for PRA but not PAC in EH patients. Age-specific RIs were established accordingly. CONCLUSIONS: This study presented age-specific LC-MS/MS RIs of PRA and PAC for both normotensive and EH populations for local Chinese in Hong Kong.

Preliminary study on ultrasound-guided high-intensity focused ultrasound ablation for treatment of broad ligament uterine fibroids.

OBJECTIVE: To investigate the feasibility, efficacy and safety of ultrasound-guided high-intensity focused ultrasound (HIFU) ablation for treating the broad ligament uterine fibroid (BLUF). METHODS: A total of 236 patients with symptomatic uterine fibroids were enrolled and treated with JC-200 extracorporeal ultrasound-guided HIFU under conscious sedation between January 2017 and December 2018. Of them, data of 12 patients with 13 broad ligament fibroids were retrospectively analyzed. The patients' mean age was 38.6 ± 6.3 years. The focused ultrasound target was deployed and moved from the deeper layer to the superficial layer of BLUFs. All patients underwent contrast-enhanced MRI (CE-MRI) before, immediate post-operation, and six months after the HIFU ablation procedure. The fibroid size, non-perfusion volume (NPV) ratio, the reduction of fibroid volumes, adverse events, symptom changes, and abnormal MRI findings associated with the HIFU treatment were analyzed. RESULTS: Ultrasound-guided HIFU ablation in the twelve patients was technically successful with one session treatment. The mean longest diameter of BLUFs was 6.2 ± 2.3 cm. The mean NPV ratio of fibroids was 84.08%± 9.4%. After HIFU ablation, lower abdominal pain occurred in 7 cases, sacrococcygeal pain in 3 cases, and mild skin pain in 6 cases. There were no severe adverse events and complications associated with the treatment. At 6 months post-treatment follow-up, the mean fibroid volume decreased by 56.2%± 9.0% (p < 0.05), and the symptoms related to broad ligament fibroids were improved or disappeared. CONCLUSIONS: Ultrasound-guided high-intensity focused ultrasound ablation is feasible, effective, and safe for treating broad ligament fibroids.

Structural conditions on complex networks for the Michaelis-Menten input-output response.

The Michaelis-Menten (MM) fundamental formula describes how the rate of enzyme catalysis depends on substrate concentration. The familiar hyperbolic relationship was derived by timescale separation for a network of three reactions. The same formula has subsequently been found to describe steady-state input-output responses in many biological contexts, including single-molecule enzyme kinetics, gene regulation, transcription, translation, and force generation. Previous attempts to explain its ubiquity have been limited to networks with regular structure or simplifying parametric assumptions. Here, we exploit the graph-based linear framework for timescale separation to derive general structural conditions under which the MM formula arises. The conditions require a partition of the graph into two parts, akin to a "coarse graining" into the original MM graph, and constraints on where and how the input variable occurs. Other features of the graph, including the numerical values of parameters, can remain arbitrary, thereby explaining the formula's ubiquity. For systems at thermodynamic equilibrium, we derive a necessary and sufficient condition. For systems away from thermodynamic equilibrium, especially those with irreversible reactions, distinct structural conditions arise and a general characterization remains open. Nevertheless, our results accommodate, in much greater generality, all examples known to us in the literature.

Opportunistic muscle measurements on staging chest CT for extremity and truncal soft tissue sarcoma are associated with survival.

BACKGROUND AND OBJECTIVES: Computed tomography (CT) measurements of sarcopenia have been proposed as biomarkers associated with outcomes in various cancers and have typically been evaluated at the L3 vertebral level. However, staging imaging for patients with extremity and truncal soft tissue sarcoma (STS) often only includes chest CT imaging which precludes evaluation at L3. Therefore, we sought to evaluate muscle metrics at T12 on standard staging chest CT scans and evaluate for correlation with overall and event-free survival in patients with STS. METHODS: CT chest imaging for 89 patients with intermediate and high-grade STS (53 male, 36 female; 58.5 ± 19.0 years old, follow-up 37.4 ± 27.1 months) was reviewed on PACS at T12 for skeletal muscle density (SMD) and skeletal muscle index (SMI). RESULTS: Overall survival increased with increased SMD on univariate (hazard ratio [HR] = 0.61 [0.43, 0.86]) and age-adjusted analysis (HR = 0.65 [0.42, 0.89]. Event-free survival also increased with increased SMD in univariate analyses (HR = 0.68 [0.49, 0.95]) but did not maintain significance after adjusting for age (HR = 0.68 [0.43, 1.07]). SMI was not a predictor of overall or event-free survival. CONCLUSIONS: Higher SMD measured on routinely obtained staging chest CTs in STS patients is associated with improved survival.

Mechanics and Dynamics of Bacterial Cell Lysis.

Membrane lysis, or rupture, is a cell death pathway in bacteria frequently caused by cell wall-targeting antibiotics. Although previous studies have clarified the biochemical mechanisms of antibiotic action, a physical understanding of the processes leading to lysis remains lacking. Here, we analyze the dynamics of membrane bulging and lysis in Escherichia coli, in which the formation of an initial, partially subtended spherical bulge ("bulging") after cell wall digestion occurs on a characteristic timescale of 1 s and the growth of the bulge ("swelling") occurs on a slower characteristic timescale of 100 s. We show that bulging can be energetically favorable due to the relaxation of the entropic and stretching energies of the inner membrane, cell wall, and outer membrane and that the experimentally observed timescales are consistent with model predictions. We then show that swelling is mediated by the enlargement of wall defects, after which cell lysis is consistent with both the inner and outer membranes exceeding characteristic estimates of the yield areal strains of biological membranes. These results contrast biological membrane physics and the physics of thin, rigid shells. They also have implications for cellular morphogenesis and antibiotic discovery across different species of bacteria.

The Lateral Habenula and Its Input to the Rostromedial Tegmental Nucleus Mediates Outcome-Specific Conditioned Inhibition.

Animals can readily learn that stimuli predict the absence of specific appetitive outcomes; however, the neural substrates underlying such outcome-specific conditioned inhibition remain largely unexplored. Here, using female and male rats as subjects, we examined the involvement of the lateral habenula (LHb) and of its inputs onto the rostromedial tegmental nucleus (RMTg) in inhibitory learning. In these experiments, we used backward conditioning and contingency reversal to establish outcome-specific conditioned inhibitors for two distinct appetitive outcomes. Then, using the Pavlovian-instrumental transfer paradigm, we assessed the effects of manipulations of the LHb and the LHb-RMTg pathway on that inhibitory encoding. In control animals, we found that an outcome-specific conditioned inhibitor biased choice away from actions delivering that outcome and toward actions earning other outcomes. Importantly, this bias was abolished by both electrolytic lesions of the LHb and selective ablation of LHb neurons using Cre-dependent Caspase3 expression in Cre-expressing neurons projecting to the RMTg. This deficit was specific to conditioned inhibition; an excitatory predictor of a specific outcome-biased choice toward actions delivering the same outcome to a similar degree whether the LHb or the LHb-RMTg network was intact or not. LHb lesions also disrupted the ability of animals to inhibit previously encoded stimulus-outcome contingencies after their reversal, pointing to a critical role of the LHb and of its inputs onto the RMTg in outcome-specific conditioned inhibition in appetitive settings. These findings are consistent with the developing view that the LHb promotes a negative reward prediction error in Pavlovian conditioning.SIGNIFICANCE STATEMENT Stimuli that positively or negatively predict rewarding outcomes influence choice between actions that deliver those outcomes. Previous studies have found that a positive predictor of a specific outcome biases choice toward actions delivering that outcome. In contrast, a negative predictor of an outcome biases choice away from actions earning that outcome and toward other actions. Here we reveal that the lateral habenula is critical for negative predictors, but not positive predictors, to affect choice. Furthermore, these effects were found to require activation of lateral habenula inputs to the rostromedial tegmental nucleus. These results are consistent with the view that the lateral habenula establishes inhibitory relationships between stimuli and food outcomes and computes a negative prediction error in Pavlovian conditioning.

Prenatal Diagnosis Innovation: Genome Sequencing of Maternal Plasma.

Noninvasive prenatal testing (NIPT) is accomplished by analysis of circulating cell-free fetal nucleic acids in maternal plasma. The advent of massively parallel sequencing (MPS) has enabled NIPT of chromosomal aneuploidies with unprecedented robustness, and these tests are now widely available for clinical use. Moreover, MPS-based NIPT of subchromosomal deletions/duplications and single-gene disorders has also been achieved, and the number of applications is growing. In addition to specific fetal genetic disorders, the whole fetal genome, transcriptome, and methylome have been revealed by deep sequencing of maternal plasma. The analysis of the fetal transcriptome and methylome may yield valuable information on fetal and maternal health. With continued improvement in sequencing technology and reduction in sequencing costs, the analysis of cell-free nucleic acids would play an increasingly important role in prenatal screening, diagnosis, monitoring, and risk stratification of fetal as well as maternal conditions.

Pelvic Tilt Evaluation From Frontal Radiographs: The Validity, Interobserver Reliability and Intraobserver Reproducibility of the Sacro-Femoral-Pubic Parameter.

BACKGROUND: The sacro-femoral-pubic (SFP) parameter, calculated using the SFP angle measured on a frontal pelvis radiograph, has previously been shown to have a strong correlation with sagittal pelvic tilt (PT) measured on a lateral x-ray. The purpose of this study is to assess the validity, interobserver reliability and intraobserver reproducibility of the SFP parameter in predicting the sagittal PT. METHODS: This is a retrospective study of 100 patients with frontal and lateral radiographs of the pelvis. Two observers independently measured the SFP angle on frontal x-ray (midpoint of S1 end plate to centroid of acetabula to upper midpoint of the pubic symphysis) and PT on lateral x-ray (midpoint of sacral plate to the centroid of acetabula to vertical plane). The SFP parameter was defined using the equation: SFP parameter = 75 - SFP angle. The interobserver reliability and intraobserver reproducibility were calculated using interclass correlation coefficient (ICC). Validity of the SFP parameter was calculated using Pearson correlation coefficient. RESULTS: The intraobserver reproducibility of the SFP parameter was excellent (ICC >0.90) for both observers. The interobserver reliability of all measurements was substantial for the SFP parameter (ICC >0.80) and PT (ICC >0.70). The concurrent validity of the SFP parameter was substantial (r = 0.70). CONCLUSION: Calculating PT from a frontal radiograph using the equation for the SFP parameter is a valid, reliable, and reproducible formula that may be used to predict sagittal PT.

A framework for modelling gene regulation which accommodates non-equilibrium mechanisms.

BACKGROUND: Gene regulation has, for the most part, been quantitatively analysed by assuming that regulatory mechanisms operate at thermodynamic equilibrium. This formalism was originally developed to analyse the binding and unbinding of transcription factors from naked DNA in eubacteria. Although widely used, it has made it difficult to understand the role of energy-dissipating, epigenetic mechanisms, such as DNA methylation, nucleosome remodelling and post-translational modification of histones and co-regulators, which act together with transcription factors to regulate gene expression in eukaryotes. RESULTS: Here, we introduce a graph-based framework that can accommodate non-equilibrium mechanisms. A gene-regulatory system is described as a graph, which specifies the DNA microstates (vertices), the transitions between microstates (edges) and the transition rates (edge labels). The graph yields a stochastic master equation for how microstate probabilities change over time. We show that this framework has broad scope by providing new insights into three very different ad hoc models, of steroid-hormone responsive genes, of inherently bounded chromatin domains and of the yeast PHO5 gene. We find, moreover, surprising complexity in the regulation of PHO5, which has not yet been experimentally explored, and we show that this complexity is an inherent feature of being away from equilibrium. At equilibrium, microstate probabilities do not depend on how a microstate is reached but, away from equilibrium, each path to a microstate can contribute to its steady-state probability. Systems that are far from equilibrium thereby become dependent on history and the resulting complexity is a fundamental challenge. To begin addressing this, we introduce a graph-based concept of independence, which can be applied to sub-systems that are far from equilibrium, and prove that history-dependent complexity can be circumvented when sub-systems operate independently. CONCLUSIONS: As epigenomic data become increasingly available, we anticipate that gene function will come to be represented by graphs, as gene structure has been represented by sequences, and that the methods introduced here will provide a broader foundation for understanding how genes work.

Discovery of antibiotics that selectively kill metabolically dormant bacteria.

There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally distinct compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.

Synthetic control of actin polymerization and symmetry breaking in active protocells.

Nonlinear biomolecular interactions on membranes drive membrane remodeling crucial for biological processes including chemotaxis, cytokinesis, and endocytosis. The complexity of biomolecular interactions, their redundancy, and the importance of spatiotemporal context in membrane organization impede understanding of the physical principles governing membrane mechanics. Developing a minimal in vitro system that mimics molecular signaling and membrane remodeling while maintaining physiological fidelity poses a major challenge. Inspired by chemotaxis, we reconstructed chemically regulated actin polymerization inside vesicles, guiding membrane self-organization. An external, undirected chemical input induced directed actin polymerization and membrane deformation uncorrelated with upstream biochemical cues, suggesting symmetry breaking. A biophysical model incorporating actin dynamics and membrane mechanics proposes that uneven actin distributions cause nonlinear membrane deformations, consistent with experimental findings. This protocellular system illuminates the interplay between actin dynamics and membrane shape during symmetry breaking, offering insights into chemotaxis and other cell biological processes.

Hemoglobin A1c in early pregnancy to identify preexisting diabetes mellitus and women at risk of hyperglycemic pregnancy complications.

BACKGROUND: Unrecognized diabetes mellitus during pregnancy could pose serious maternal and neonatal complications. A hemoglobin A1c level of ≥6.5% was used to diagnose both diabetes mellitus in nonpregnant individuals and diabetes in pregnancy. As the hemoglobin A1c level could be influenced by maternal physiological changes, the optimal cutoff in early pregnancy to detect women with diabetes in pregnancy and associated complications remains unclear. OBJECTIVE: This study aimed to evaluate the diagnostic performance of various hemoglobin A1c levels and the optimal hemoglobin A1c cutoff to identify mothers with diabetes in pregnancy diagnosed by the gold standard 75 g oral glucose tolerance test before 24 weeks of gestation. In addition, the pregnancy and neonatal outcomes were compared using the optimal hemoglobin A1c cutoff. STUDY DESIGN: A retrospective cohort study was conducted between 2004 and 2019. Women with at least 1 risk factor of gestational diabetes mellitus received an oral glucose tolerance test before 24 weeks of gestation. Terminology of hyperglycemia first detected during pregnancy by oral glucose tolerance test was classified as either diabetes in pregnancy or gestational diabetes mellitus following the World Health Organization's recommendation. Women who met the diagnostic criteria of diabetes in pregnancy and early-onset gestational diabetes mellitus (ie, before 24 weeks of gestation) and had a paired hemoglobin A1c measurement within 4 weeks of their early oral glucose tolerance test were studied. Sensitivity, specificity, and positive and negative predictive values at various hemoglobin A1c cutoffs were calculated for the detection of diabetes in pregnancy. The optimal hemoglobin A1c level was identified from the constructed receiver operating characteristic curves. Multivariate binary logistic regression analyses were performed to calculate the unadjusted and adjusted odds ratios for pregnancy complications. RESULTS: There were 63,111 deliveries, and 22,949 women underwent an oral glucose tolerance test before 24 weeks of gestation. A total of 157 and 3210 women met the diagnostic criteria of diabetes in pregnancy and early-onset gestational diabetes mellitus using an oral glucose tolerance test, respectively. Only 346 participants had a paired hemoglobin A1c and oral glucose tolerance test measurement (82 cases with diabetes in pregnancy and 264 cases with early-onset gestational diabetes mellitus). The receiver operating characteristic curve identified an optimal hemoglobin A1c cutoff of 5.7% to diagnose diabetes in pregnancy, with a sensitivity of 64.6%, specificity of 81.1%, positive predictive value of 51.5%, and negative predictive value of 88.1%. A hemoglobin A1c cutoff of either 5.9% or 6.5% could miss 47.6% or 73.2% of women with diabetes in pregnancy. In multivariate logistic regression analysis, a hemoglobin A1c level of ≥5.7% increased the risk of maternal insulin use (adjusted odds ratio, 6.69; 95% confidence interval, 3.44-12.99), macrosomia (adjusted odds ratio, 7.43; 95% confidence interval, 1.90-29.00), and shoulder dystocia (adjusted odds ratio, 6.56; 95% confidence interval, 1.161-37.03). CONCLUSION: The optimal hemoglobin A1c cutoff to detect diabetes in pregnancy diagnosed using an oral glucose tolerance test before 24 weeks of gestation was 5.7%, but this cutoff could not reliably identify diabetes in pregnancy owing to the low sensitivity. However, an early hemoglobin A1c level of ≥5.7% indicated increased risks of pregnancy and neonatal complications.

Combined oestrogen and progesterone for preventing miscarriage.

BACKGROUND: Historically, oestrogen and progesterone were each commonly used to save threatened pregnancies. In the 1940s it was postulated that their combined use would be synergistic and thereby led to the rationale of combined therapy for women who risked miscarriage. OBJECTIVES: To determine the efficacy and safety of combined oestrogen and progesterone therapy to prevent miscarriage. SEARCH METHODS: We searched the Cochrane Pregnancy and Childbirth Group's Trials Register (23 June 2013) CENTRAL (OVID) (The Cochrane Library 2013, Issue 6 of 12), MEDLINE (OVID) (1946 to June Week 2 2013), OLDMEDLINE (1946 to 1965), Embase (1974 to Week 25 2013), Embase Classic (1947 to 1973), CINAHL (1994 to 23 June 2013) and reference lists of retrieved studies. SELECTION CRITERIA: We included randomised controlled trials that assessed the effectiveness of combined oestrogen and progesterone for preventing miscarriage. We included one stratified randomised trial and one quasi-randomised trials. Cluster-randomised trials were eligible for inclusion but none were identified. We excluded studies published only as abstracts.We included studies that compared oestrogen and progesterone versus placebo or no intervention. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed trials for inclusion and assessed trial quality. Two review authors extracted data. Data were checked for accuracy. MAIN RESULTS: Two trials (281 pregnancies and 282 fetuses) met our inclusion criteria. However, the two trials had significant clinical and methodological heterogeneity such that a meta-analysis combining trial data was considered inappropriate.One trial (involving 161 pregnancies) was based on women with a history of diabetes. It showed no statistically significant difference between using combined oestrogen and progestogen and using placebo for all our proposed primary outcomes, namely, miscarriage (risk ratio (RR) 0.95, 95% confidence interval (CI) 0.32 to 2.80), perinatal death (RR 0.94, 95% CI 0.53 to 1.69) and preterm birth (less than 34 weeks of gestation) (RR 0.91, 95% CI 0.80 to 1.04). In terms of this review's secondary outcomes, use of combined oestrogen and progestogen was associated with an increased risk of maternal cancer in the reproductive system (RR 6.65, 95% CI 1.56 to 28.29). However, for the outcome of cancer other than that of the reproductive system in mothers, there was no difference between groups. Similarly, there were no differences between the combined oestrogen and progestogen group versus placebo for other secondary outcomes reported: low birthweight of less than 2500 g, genital abnormalities in the offspring, abnormalities other than genital tract in the offspring, cancer in the reproductive system in the offspring, or cancer other than of the reproductive system in the offspring.The second study was based on pregnant women who had undergone in-vitro fertilisation (IVF). This study showed no difference in the rate of miscarriage between the combined oestrogen and progesterone group and the no treatment group (RR 0.66, 95% CI 0.23 to 1.85). The study did not report on this review's other primary outcomes (perinatal death or rates of preterm birth), nor on any of our proposed secondary outcomes. AUTHORS' CONCLUSIONS: There is an insufficient evidence from randomised controlled trials to assess the use of combined oestrogen and progesterone for preventing miscarriages. We strongly recommend further research in this area.