Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis.

Antibacterial agents target the products of essential genes but rarely achieve complete target inhibition. Thus, the all-or-none definition of essentiality afforded by traditional genetic approaches fails to discern the most attractive bacterial targets: those whose incomplete inhibition results in major fitness costs. In contrast, gene "vulnerability" is a continuous, quantifiable trait that relates the magnitude of gene inhibition to the effect on bacterial fitness. We developed a CRISPR interference-based functional genomics method to systematically titrate gene expression in Mycobacterium tuberculosis (Mtb) and monitor fitness outcomes. We identified highly vulnerable genes in various processes, including novel targets unexplored for drug discovery. Equally important, we identified invulnerable essential genes, potentially explaining failed drug discovery efforts. Comparison of vulnerability between the reference and a hypervirulent Mtb isolate revealed incomplete conservation of vulnerability and that differential vulnerability can predict differential antibacterial susceptibility. Our results quantitatively redefine essential bacterial processes and identify high-value targets for drug development.

Ketamine versus etomidate as an induction agent for tracheal intubation in critically ill adults: a Bayesian meta-analysis.

BACKGROUND: Tracheal intubation is a high-risk intervention commonly performed in critically ill patients. Due to its favorable cardiovascular profile, ketamine is considered less likely to compromise clinical outcomes. This meta-analysis aimed to assess whether ketamine, compared with other agents, reduces mortality in critically ill patients undergoing intubation. METHODS: We searched MEDLINE, Embase, and the Cochrane Library from inception until April 27, 2023, for randomized controlled trials and matched observational studies comparing ketamine with any control in critically ill patients as an induction agent. The primary outcome was mortality at the longest follow-up available, and the secondary outcomes included Sequential Organ Failure Assessment score, ventilator-free days at day 28, vasopressor-free days at day 28, post-induction mean arterial pressure, and successful intubation on the first attempt. For the primary outcome, we used a Bayesian random-effects meta-analysis on the risk ratio (RR) scale with a weakly informative neutral prior corresponding to a mean estimate of no difference with 95% probability; the estimated effect size will fall between a relative risk of 0.25 and 4. The RR and 95% credible interval (CrI) were used to estimate the probability of mortality reduction (RR < 1). The secondary outcomes were assessed with a frequentist random-effects model. We registered this study in Open Science Framework ( https://osf.io/2vf79/ ). RESULTS: We included seven randomized trials and one propensity-matched study totaling 2978 patients. Etomidate was the comparator in all the identified studies. The probability that ketamine reduced mortality was 83.2% (376/1475 [25%] vs. 411/1503 [27%]; RR, 0.93; 95% CrI, 0.79-1.08), which was confirmed by a subgroup analysis excluding studies with a high risk of bias. No significant difference was observed in any secondary outcomes. CONCLUSIONS: All of the included studies evaluated ketamine versus etomidate among critically ill adults requiring tracheal intubation. This meta-analysis showed a moderate probability that induction with ketamine is associated with a reduced risk of mortality.

Statistically significant differences versus convincing evidence of real treatment effects: an analysis of the false positive risk for single-centre trials in anaesthesia.

BACKGROUND: The American Statistical Association has highlighted problems with null hypothesis significance testing and outlined alternative approaches that may 'supplement or even replace P-values'. One alternative is to report the false positive risk (FPR), which quantifies the chance the null hypothesis is true when the result is statistically significant. METHODS: We reviewed single-centre, randomised trials in 10 anaesthesia journals over 6 yr where differences in a primary binary outcome were statistically significant. We calculated a Bayes factor by two methods (Gunel, Kass). From the Bayes factor we calculated the FPR for different prior beliefs for a real treatment effect. Prior beliefs were quantified by assigning pretest probabilities to the null and alternative hypotheses. RESULTS: For equal pretest probabilities of 0.5, the median (inter-quartile range [IQR]) FPR was 6% (1-22%) by the Gunel method and 6% (1-19%) by the Kass method. One in five trials had an FPR ≥20%. For trials reporting P-values 0.01-0.05, the median (IQR) FPR was 25% (16-30%) by the Gunel method and 20% (16-25%) by the Kass method. More than 90% of trials reporting P-values 0.01-0.05 required a pretest probability >0.5 to achieve an FPR of 5%. The median (IQR) difference in the FPR calculated by the two methods was 0% (0-2%). CONCLUSIONS: Our findings suggest that a substantial proportion of single-centre trials in anaesthesia reporting statistically significant differences provide limited evidence of real treatment effects, or, alternatively, required an implausibly high prior belief in a real treatment effect. CLINICAL TRIAL REGISTRATION: PROSPERO (CRD42023350783).

Estimating effects of whole grain consumption on type 2 diabetes, colorectal cancer and cardiovascular disease: a burden of proof study.

BACKGROUND: Previous studies on whole grain consumption had inconsistent findings and lacked quantitative assessments of evidence quality. Therefore, we aimed to summarize updated findings using the Burden of Proof analysis (BPRF) to investigate the relationship of whole grain consumption on type 2 diabetes (T2D), colorectal cancer (CRC), stroke, and ischemic heart disease (IHD). METHODS: We conducted a literature search in the Medline and Web of Science up to June 12, 2023, to identify related cohort studies and systematic reviews. The mean RR (relative risk) curve and uncertainty intervals (UIs), BPRF function, risk-outcome score (ROS), and the theoretical minimum risk exposure level (TMREL) were estimated to evaluate the level of four risk-outcome pairs. RESULTS: In total, 27 prospective cohorts were included in our analysis. Consuming whole grain at the range of TMREL (118.5-148.1 g per day) was associated with lower risks: T2D (declined by 37.3%, 95% UI: 5.8 to 59.5), CRC (declined by 17.3%, 6.5 to 27.7), stroke (declined by 21.8%, 7.3 to 35.1), and IHD (declined by 36.9%, 7.1 to 58.0). For all outcomes except stroke, we observed a non-linear, monotonic decrease as whole grain consumption increased; For stroke, it followed a J-shaped curve (the greatest decline in the risk of stroke at consuming 100 g whole grain for a day). The relationships between whole grain consumption and four diseases are all two-star pairs (ROS: 0.087, 0.068, 0.062, 0.095 for T2D, CRC, stroke, and IHD, respectively). CONCLUSION: Consuming 100 g of whole grains per day offers broad protective benefits. However, exceeding this threshold may diminish the protective effects against stroke. Our findings endorse replacing refined grains with whole grains as the main source of daily carbohydrates. REGISTRY AND REGISTRY NUMBER FOR SYSTEMATIC REVIEWS OR META-ANALYSES: We have registered our research in PROSPERO, and the identifier of our meta-analyses is CRD42023447345.

Therapeutic misunderstandings in modern research.

Clinical trials play a crucial role in generating evidence about healthcare interventions and improving outcomes for current and future patients. For individual trial participants, however, there are inevitably trade-offs involved in clinical trial participation, given that trials have traditionally been designed to benefit future patient populations rather than to offer personalised care. Failure to understand the distinction between research and clinical care and the likelihood of benefit from participation in clinical trials has been termed the 'therapeutic misconception'. The evolution of the clinical trials landscape, including greater integration of clinical trials into healthcare and development of novel trial methodologies, may reinforce the significance of the therapeutic misconception and other forms of misunderstanding while at the same time (paradoxically) challenging its salience. Using cancer clinical trials as an exemplar, we describe how methodological changes in early- and late-phase clinical trial designs, as well as changes in the design and delivery of healthcare, impact upon the therapeutic misconception. We suggest that this provides an impetus to re-examine the ethics of clinical research, particularly in relation to trial access, participant selection, communication and consent, and role delineation.

The Bayesian approach for real-world implementation of plasma p-tau217 in tertiary care memory clinics in Thailand.

INTRODUCTION: Plasma phosphorylated tau (p-tau)217 is a promising biomarker for Alzheimer's disease (AD) diagnosis, but its clinical implementation remains challenging. We propose a strategy based on Bayes' theorem and test it in real-life memory clinics. METHODS: Memory clinic patients were evaluated by neurocognitive specialists for prespecified diagnosis and subsequently underwent blood collection for p-tau217, cerebrospinal fluid, or amyloid positron emission tomography. Using cross-validation, the Bayesian approach (pretest probability × individualized likelihood ratio) was compared to other models for AD diagnosis. RESULTS: The Bayesian strategy demonstrated an area under the receiver operating characteristic curve (AUC) of 0.98 (95% confidence interval [CI]: 0.96-1.0), significantly outperforming multivariable logistic regression (p-tau217, age, apolipoprotein E; AUC 0.95, p = 0.024) and p-tau217 alone (AUC = 0.94, p = 0.007). When applying the two-threshold approach, the Bayesian strategy yielded an accuracy of 0.94 (95% CI: 0.88-1.0) without requiring confirmatory tests in 62.9% of the iterations. DISCUSSION: The Bayesian strategy offers an effective and flexible approach to address the limitations of plasma p-tau217 in clinical practice. HIGHLIGHTS: Incorporating pretest probability into the interpretation of plasma phosphorylated tau (p-tau)217 improves the diagnostic performance significantly. The strategy could obviate the need for confirmatory testing in most of the patients. Plasma p-tau217 proves useful as a biomarker for Alzheimer's disease in low- and middle-income country such as Thailand.

A "Prediction - Detection - Judgment" framework for sudden water contamination event detection with online monitoring.

The contamination detection technology helps in water quality management and protection in surface water. It is important to detect sudden contamination events timely from dynamic variations due to various interference factors in online water quality monitoring data. In this study, a framework named "Prediction - Detection - Judgment" is proposed with a method framework of "Time series increment - Hierarchical clustering - Bayes' theorem model". Time to detection is used as an evaluation index of contamination detection methods, along with the probability of detection and false alarm rate. The proposed method is tested with available public data and further applied in a monitoring site of a river. Results showed that the method could detect the contamination events with a 100% probability of detection, a 17% false alarm rate and a time to detection close to 4 monitoring intervals. The proposed index time to detection evaluates the timeliness of the method, and timely detection ensures that contamination events can be responded to and dealt with in time. The site application also demonstrates the feasibility and practicability of the framework proposed in this study and its potential for extensive implementation.

Intrinsic Information-Theoretic Models.

With this follow-up paper, we continue developing a mathematical framework based on information geometry for representing physical objects. The long-term goal is to lay down informational foundations for physics, especially quantum physics. We assume that we can now model information sources as univariate normal probability distributions N (µ, σ0), as before, but with a constant σ0 not necessarily equal to 1. Then, we also relaxed the independence condition when modeling m sources of information. Now, we model m sources with a multivariate normal probability distribution Nm(µ,Σ0) with a constant variance-covariance matrix Σ0 not necessarily diagonal, i.e., with covariance values different to 0, which leads to the concept of modes rather than sources. Invoking Schrödinger's equation, we can still break the information into m quantum harmonic oscillators, one for each mode, and with energy levels independent of the values of σ0, altogether leading to the concept of "intrinsic". Similarly, as in our previous work with the estimator's variance, we found that the expectation of the quadratic Mahalanobis distance to the sample mean equals the energy levels of the quantum harmonic oscillator, being the minimum quadratic Mahalanobis distance at the minimum energy level of the oscillator and reaching the "intrinsic" Cramér-Rao lower bound at the lowest energy level. Also, we demonstrate that the global probability density function of the collective mode of a set of m quantum harmonic oscillators at the lowest energy level still equals the posterior probability distribution calculated using Bayes' theorem from the sources of information for all data values, taking as a prior the Riemannian volume of the informative metric. While these new assumptions certainly add complexity to the mathematical framework, the results proven are invariant under transformations, leading to the concept of "intrinsic" information-theoretic models, which are essential for developing physics.

Comparison of Bayesian Spatiotemporal Models for Small-Area Life Expectancy: A Simulation Study.

The purpose of this study was to assess the precision, uncertainty, and normality of small-area life expectancy estimates calculated using Bayesian spatiotemporal models. We hypothesized 6 scenarios in which all 247 districts of South Korea had the same year-specific female population of 500, 1,000, 2,000, 5,000, 10,000, and 25,000 individuals during the study period (2013-2017). We generated 1,000 hypothetical data sets for each scenario and calculated district-year life expectancies. The precision and uncertainty of life expectancy estimates were compared between 2 Bayesian spatiotemporal models and the traditional method and Bayesian spatial models. We examined the normality of the life expectancy distributions generated by each method and investigated an optimal cutoff value for the comparisons. The Bayesian spatiotemporal models produced precise life expectancy estimates. However, the 95% uncertainty interval contained the true value with a probability of less than 95%. The Bayesian spatiotemporal models violated the normality assumption in scenarios with small population sizes. Therefore, life expectancy comparisons should be conducted using a cutoff value that minimizes false-positive and false-negative rates. We propose 0.8 as a cutoff value for determining the statistical significance of the difference in life expectancy.

Where Do CABs Exist? Verification of a specific region containing concave Actin Bundles (CABs) in a 3-Dimensional confocal image.

CABs (Concave Actin Bundles) are oriented against the scaffold transversally in a manner different from traditional longitudinal F-actin bundles. CABs are present in a specific area, and do not exist in random areas. Biologically, CABs are developed to attach cells to fibers firmly so that CABs are found near cells. Based on this knowledge, we closely examined 3D confocal microcopy images containing fiber scaffolds, actin, and cells. Then, we assumed that the areas containing high values of compactness of fiber, compactness of actin, and density of cells would have many numbers of CABs.In this research, we wanted to prove this assumption. We first incorporated a two-point correlation function to define a measure of compactness. Then, we used the Bayes' theorem to prove the above assumption. As the assumption, our results verified that CABs exist in an area of high compactness of a fiber network, high compactness of actin distribution, and high density of cells. Thus, we concluded that CABs are developed to attach cells to a fibrillar scaffold firmly. This finding may be further verified mathematically in future studies.

Personalized stratification of pregnancy care for small for gestational age neonates from biophysical markers at midgestation.

BACKGROUND: Antenatal identification of pregnancies at high risk of delivering small for gestational age neonates may improve the management of the condition and reduce the associated adverse perinatal outcomes. In a series of publications, we have developed a new competing-risks model for small for gestational age prediction, and we demonstrated that the new approach has a superior performance to that of the traditional methods. The next step in shaping the appropriate management of small for gestational age is the timely assessment of these high-risk pregnancies according to an antenatal stratification plan. OBJECTIVE: This study aimed to demonstrate the stratification of pregnancy care based on individual patient risk derived from the application of the competing-risks model for small for gestational age that combines maternal factors with sonographic estimated fetal weight and uterine artery pulsatility index at midgestation. STUDY DESIGN: This was a prospective observational study of 96,678 singleton pregnancies undergoing routine ultrasound examination at 19 to 24 weeks of gestation, which included recording of estimated fetal weight and measurement of uterine artery pulsatility index. The competing-risks model for small for gestational age was used to create a patient-specific stratification curve capable to define a specific timing for a repeated ultrasound examination after 24 weeks. We examined different stratification plans with the intention of detecting approximately 80%, 85%, 90%, and 95% of small for gestational age neonates with birthweight <3rd and <10th percentiles at any gestational age at delivery until 36 weeks; all pregnancies would be offered a routine ultrasound examination at 36 weeks. RESULTS: The stratification of pregnancy care for small for gestational age can be based on a patient-specific stratification curve. Factors from maternal history, low estimated fetal weight, and increased uterine artery pulsatility index shift the personalized risk curve toward higher risks. The degree of shifting defines the timing for assessment for each pregnancy. If the objective of our antenatal plan was to detect 80%, 85%, 90%, and 95% of small for gestational age neonates at any gestational age at delivery until 36 weeks, the median (range) proportions (percentages) of population examined per week would be 3.15 (1.9-3.7), 3.85 (2.7-4.5), 4.75 (4.0-5.4), and 6.45 (3.7-8.0) for small for gestational age <3rd percentile and 3.8 (2.5-4.6), 4.6 (3.6-5.4), 5.7 (3.8-6.4), and 7.35 (3.3-9.8) for small for gestational age <10th percentile, respectively. CONCLUSION: The competing-risks model provides an effective personalized continuous stratification of pregnancy care for small for gestational age which is based on individual characteristics and biophysical marker levels recorded at the midgestation scan.

Incidence and Bayesian Mapping of Myeloid Hematologic Malignancies in Sardinia, Italy.

BACKGROUND: The epidemiology of myeloid hematologic malignancies in Italy has been poorly investigated. METHODS: We used a validated database of 1974-2003 incident cases of hematologic malignancies among the resident population (all ages) of Sardinia, Italy, to describe the incidence of myeloid malignancies overall (N = 4389 cases) and by subtype. We investigated the time trend of acute myeloid leukemia (N = 1227 cases), chronic myeloid leukemia (N = 613 cases), and myelodysplastic syndrome (N = 1296 cases), and used Bayesian methods to explore their geographic spread, and Poisson regression analysis to estimate their association with environmental and socio-economic factors. RESULTS: The annual standardized (world population) incidence rate (IR) of myeloid malignancies over the study period was 6.5 per 100,000 (95% CI 6.2-6.7). Myelodysplastic syndromes were the most prevalent subgroup (IR = 1.7, 95% CI 1.5-1.8). Incidence of all myeloid malignancies combined increased sharply during the study period with an annual percent change (APC) of 10.06% (95% CI 9.51-10.61), 19.77% for myelodysplastic syndromes (95% CI 19.63-19.91), and 3.18% (95% CI 2.99-3.37) for acute myeloid leukemia. Chronic myeloid leukemia did not show an upward trend. Apart from sporadic excesses in small rural communities and the major urban area, there was no evidence of spatial clustering. The risk of myeloid malignancies increased with increasing prevalence of sheep breeding. CONCLUSIONS: Our results might prompt further research on the local genetic and environmental determinants of myeloid hematologic malignancies.

Shared and unique imaging-derived endo-phenotypes of two typical antidepressant-applicative depressive patients.

OBJECTIVES: Determining the clinical homogeneous and heterogeneous sets among depressive patients is the key to facilitate individual-level treatment decision. METHODS: The diffusion tensor imaging (DTI) data of 62 patients with major depressive disorder (MDD) and 39 healthy controls were used to construct a Latent Dirichlet Allocation (LDA) Bayesian model. Another 48 MDD patients were used to verify the robustness. The LDA model was employed to identify both shared and unique imaging-derived factors of two typically antidepressant-targeted depressive patients, selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs). Furthermore, we applied canonical correlation analysis (CCA) between each factor loading and Hamilton depression rating scale (HAMD) sub-score, to explore the potential neurophysiological significance of each factor. RESULTS: The results revealed the imaging-derived connectional fingerprint of all patients could be situated along three latent factor dimensions; such results were also verified by the out-of-sample dataset. Factor 1, uniquely expressed by SNRI-targeted patients, was associated with retardation (r = 0.4, p = 0.037) and characterized by coupling patterns between default mode network and cognitive control network. Factor 3, uniquely expressed by SSRI-targeted patients, was associated with cognitive impairment (r = 0.36, p = 0.047) and characterized by coupling patterns within cognitive control and attention network, and the connectivity between threat and reward network. Shared factor 2, characterized by coupling patterns within default mode network, was associated with anxiety (r = 0.54, p = 0.005) and sleep disturbance (r = 0.37, p = 0.032). CONCLUSIONS: Our findings suggested that quantification of both homogeneity and heterogeneity within MDD may have the potential to inform rational design of pharmacological therapies. KEY POINTS: • The shared and unique manifestations guiding pharmacotherapy of depressive patients are caused by the homogeneity and heterogeneity of underlying structural connections of the brain. • Both shared and unique factor loadings were found in different antidepressant-targeted patients. • Significant correlations between factor loading and HAMD sub-scores were found.

Network Analysis of Endovascular Treatment Strategies for Femoropopliteal Arterial Occlusive Disease.

PURPOSE: Endovascular treatment of femoropopliteal arterial diseases remains controversial. We conducted a Bayesian network meta-analysis of randomized controlled trials aiming to investigate the efficacy differences between paclitaxel- or sirolimus-eluting stents, covered stents, drug-coated balloons, bare metal stents, and percutaneous transluminal angioplasty. METHOD: MEDLINE, Embase, Ovid, and other relevant online material were searched up to October 21, 2020. Primary endpoints were primary patency and target lesion revascularization at 6, 12, and more than 24 months. RESULTS: Thirty-eight eligible trials included 6026 patients. In terms of primary patency, drug eluting stents were ranked as the most effective treatment based on the surface under the cumulative ranking curve values at 6 (80.6), 12 (78.4), and more than 24 months (96.5) of follow-ups. In terms of target lesion revascularization, drug eluting stents were ranked as the most effective treatment based on the surface under the cumulative ranking curve values at 6 (90.3), 12 (71.3), and more than 24 months (82.1) of follow-ups. Covered stents and bare metal stents had higher ranks in target lesion revascularization than those in primary patency. Sirolimus stents had a higher rank than paclitaxel stents. CONCLUSION: Drug eluting stents showed encouraging results in primary patency rates and freedom from target lesion revascularization at all phases of follow-up for femoropopliteal arterial diseases. Sirolimus stents appear to be more effective in femoropopliteal segment than paclitaxel stent.

Declarative Learning, Priming, and Procedural Learning Performances comparing Individuals with Amnestic Mild Cognitive Impairment, and Cognitively Unimpaired Older Adults.

OBJECTIVE: While declarative learning is dependent on the hippocampus, procedural learning and repetition priming can operate independently from the hippocampus, making them potential targets for behavioral interventions that utilize non-declarative memory systems to compensate for the declarative learning deficits associated with hippocampal insult. Few studies have assessed procedural learning and repetition priming in individuals with amnestic mild cognitive impairment (aMCI). METHOD: This study offers an overview across declarative, conceptual repetition priming, and procedural learning tasks by providing between-group effect sizes and Bayes Factors (BFs) comparing individuals with aMCI and controls. Seventy-six individuals with aMCI and 83 cognitively unimpaired controls were assessed. We hypothesized to see the largest differences between individuals with aMCI and controls on declarative learning, followed by conceptual repetition priming, with the smallest differences on procedural learning. RESULTS: Consistent with our hypotheses, we found large differences between groups with supporting BFs on declarative learning. For conceptual repetition priming, we found a small-to-moderate between-group effect size and a non-conclusive BF somewhat in favor of a difference between groups. We found more variable but overall trivial differences on procedural learning tasks, with inconclusive BFs, in line with expectations. CONCLUSIONS: The current results suggest that conceptual repetition priming does not remain intact in individuals with aMCI while procedural learning may remain intact. While additional studies are needed, our results contribute to the evidence-base that suggests that procedural learning may remain spared in aMCI and helps inform behavioral interventions that aim to utilize procedural learning in this population.

Maternal hepatitis C prevalence and trends by county, US: 2016-2020.

BACKGROUND: Trends in the prevalence of hepatitis C virus (HCV) infection among women delivering live births may differ in rural vs. urban areas of the United States, but estimation of trends based on observed counts may lead to unstable estimates in rural counties due to small numbers. OBJECTIVES: The objective of the study was to use small area estimation methods to provide updated county-level prevalence estimates and, for the first time, trends in maternal HCV infection among live births by county-level rurality. METHODS: Cross-sectional natality data from 2016 to 2020 were used to estimate maternal hepatitis C prevalence using hierarchical Bayesian models with spatiotemporal random effects to produce annual county-level estimates of maternal HCV infection and trends over time. Models included a 6-level rural-urban county classification, year, maternal characteristics and county-specific covariates. Data were analysed in 2022. RESULTS: There were 90,764/18,905,314 live births (4.8 per 1000) with HCV infection reported on the birth certificate. Hepatitis C prevalence was higher among rural counties as compared to urban counties. Rural counties had the largest annual increases in maternal hepatitis C prevalence (per 1000 births) from 2016 to 2020 (micropolitan: 0.39; noncore: 0.40), with smaller increases among less densely populated urban counties (medium metro: 0.28; small metro: 0.28) and urban counties (large central metro:0.11; large fringe metro: 0.14). CONCLUSIONS: The prevalence of maternal HCV infection was the highest in rural counties, and rural counties saw the greatest average prevalence increase during 2016-2020. County-level data can help in monitoring rural-urban trends in maternal HCV infection to reduce geographic disparities.

External validation of Fetal Medicine Foundation competing-risks model for midgestation prediction of small-for-gestational-age neonates in Spanish population.

OBJECTIVE: To examine the external validity of the new Fetal Medicine Foundation (FMF) competing-risks model for prediction in midgestation of small-for-gestational-age (SGA) neonates. METHODS: This was a single-center prospective cohort study of 25 484 women with a singleton pregnancy undergoing routine ultrasound examination at 19 + 0 to 23 + 6 weeks' gestation. The FMF competing-risks model for the prediction of SGA combining maternal factors and midgestation estimated fetal weight by ultrasound scan (EFW) and uterine artery pulsatility index (UtA-PI) was used to calculate risks for different cut-offs of birth-weight percentile and gestational age at delivery. The predictive performance was evaluated in terms of discrimination and calibration. RESULTS: The validation cohort was significantly different in composition compared with the FMF cohort in which the model was developed. In the validation cohort, at a 10% false-positive rate (FPR), maternal factors, EFW and UtA-PI yielded detection rates of 69.6%, 38.7% and 31.7% for SGA < 10th percentile with delivery at < 32, < 37 and ≥ 37 weeks' gestation, respectively. The respective values for SGA < 3rd percentile were 75.7%, 48.2% and 38.1%. Detection rates in the validation cohort were similar to those reported in the FMF study for SGA with delivery at < 32 weeks but lower for SGA with delivery at < 37 and ≥ 37 weeks. Predictive performance in the validation cohort was similar to that reported in a subgroup of the FMF cohort consisting of nulliparous and Caucasian women. Detection rates in the validation cohort at a 15% FPR were 77.4%, 50.0% and 41.5% for SGA < 10th percentile with delivery at < 32, < 37 and ≥ 37 weeks, respectively, which were similar to the respective values reported in the FMF study at a 10% FPR. The model had satisfactory calibration. CONCLUSION: The new competing-risks model for midgestation prediction of SGA developed by the FMF performs well in a large independent Spanish population. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Analgesia for the Bayesian Brain: How Predictive Coding Offers Insights Into the Subjectivity of Pain.

PURPOSE OF REVIEW: In order to better treat pain, we must understand its architecture and pathways. Many modulatory approaches of pain management strategies are only poorly understood. This review aims to provide a theoretical framework of pain perception and modulation in order to assist in clinical understanding and research of analgesia and anesthesia. RECENT FINDINGS: Limitations of traditional models for pain have driven the application of new data analysis models. The Bayesian principle of predictive coding has found increasing application in neuroscientific research, providing a promising theoretical background for the principles of consciousness and perception. It can be applied to the subjective perception of pain. Pain perception can be viewed as a continuous hierarchical process of bottom-up sensory inputs colliding with top-down modulations and prior experiences, involving multiple cortical and subcortical hubs of the pain matrix. Predictive coding provides a mathematical model for this interplay.

Multicentre randomised trials in anaesthesia: an analysis using Bayesian metrics.

Are the results of randomised trials reliable and are p values and confidence intervals the best way of quantifying efficacy? Low power is common in medical research, which reduces the probability of obtaining a 'significant result' and declaring the intervention had an effect. Metrics derived from Bayesian methods may provide an insight into trial data unavailable from p values and confidence intervals. We did a structured review of multicentre trials in anaesthesia that were published in the New England Journal of Medicine, The Lancet, Journal of the American Medical Association, British Journal of Anaesthesia and Anesthesiology between February 2011 and November 2021. We documented whether trials declared a non-zero effect by an intervention on the primary outcome. We documented the expected and observed effect sizes. We calculated a Bayes factor from the published trial data indicating the probability of the data under the null hypothesis of zero effect relative to the alternative hypothesis of a non-zero effect. We used the Bayes factor to calculate the post-test probability of zero effect for the intervention (having assumed 50% belief in zero effect before the trial). We contacted all authors to estimate the costs of running the trials. The median (IQR [range]) hypothesised and observed absolute effect sizes were 7% (3-13% [0-25%]) vs. 2% (1-7% [0-24%]), respectively. Non-zero effects were declared for 12/56 outcomes (21%). The Bayes factor favouring a zero effect relative to a non-zero effect for these 12 trials was 0.000001-1.9, with post-test zero effect probabilities for the intervention of 0.0001-65%. The other 44 trials did not declare non-zero effects, with Bayes factors favouring zero effect of 1-688, and post-test probabilities of zero effect of 53-99%. The median (IQR [range]) study costs reported by 20 corresponding authors in US$ were $1,425,669 ($514,766-$2,526,807 [$120,758-$24,763,921]). We think that inadequate power and mortality as an outcome are why few trials declared non-zero effects. Bayes factors and post-test probabilities provide a useful insight into trial results, particularly when p values approximate the significance threshold.

Impact of COVID-19 Disease Control Committee (CDCC) policies on prevention of the disease using Bayes network inference in west of Iran.

BACKGROUND: The start of the COVID-19 pandemic was an emergency situation that led each country to adopt specific regional strategies to control it. Given the spread of COVID-19 disease, it is crucial to evaluate which policy is more effective in reducing disease transmission. The purpose of this study was to determine the impact of policies made by COVID-19 Disease Control Committee (CDCC) to reduce the risk of the disease in Hamadan province. METHODS: In the observational study, the data were extracted from three sources in Hamadan, west of Iran; first, the session reports of CDCC; second, information on periodic evaluations conducted by the primary health care directory in Hamadan from April to August 2021 and third, expert panel opinion. Bayes network analysis was used to determine the effect of each policy on mortality rate by GeNIe software version 2.2. RESULTS: Among the policies adopted by CDCC in Hamadan, seven policies, i.e., vaccination, limiting gatherings, social distancing, wearing a mask, job closure, travel restriction, and personal hygiene had the most impact to prevent the spread of COVID-19, respectively. In this study, the prevalence of the disease was 17.64% with the implementation of these policies. Now, if all these policies are observed 30% more, the prevalence will decrease to 14.18%. CONCLUSION: This study showed that if the seven policies (i.e., vaccination, limiting gatherings, social distancing, wearing a mask, job closure, travel restriction, and personal hygiene) are followed simultaneously in the community, the risk of contracting the disease will be greatly reduced. Therefore, in the pandemic of infectious diseases, such policies can help prevent the spread of the disease.