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Patients with stable coronary disease were randomly assigned to an initial invasive strategy with angiography and revascularization if appropriate or to medical therapy alone. At 3.2 years, there was no significant difference between the groups with respect to the estimated rate of ischemic events. The findings were sensitive to the definition of myocardial infarction.

Artificial Intelligence ( ai ) systems are precious support for decision-making, with many applications also in the medical domain. The interaction between md s and ai enjoys a renewed interest following the increased possibilities of deep learning devices. However, we still have limited evidence-based knowledge of the context, design, and psychological mechanisms that craft an optimal human– ai collaboration. In this multicentric study, 21 endoscopists reviewed 504 videos of lesions prospectively acquired from real colonoscopies. They were asked to provide an optical diagnosis with and without the assistance of an ai support system. Endoscopists were influenced by ai ( O R = 3.05 ), but not erratically: they followed the ai advice more when it was correct ( O R = 3.48 ) than incorrect ( O R = 1.85 ). Endoscopists achieved this outcome through a weighted integration of their and the ai opinions, considering the case-by-case estimations of the two reliabilities. This Bayesian-like rational behavior allowed the human– ai hybrid team to outperform both agents taken alone. We discuss the features of the human– ai interaction that determined this favorable outcome.

In a placebo-controlled trial, treatment with the preferential phosphodiesterase 4B inhibitor BI 1015550 prevented a decrease in lung function over a 12-week period in patients with idiopathic pulmonary fibrosis.

The sequencing of ancient DNA has enabled the reconstruction of speciation, migration and admixture events for extinct taxa 1 . However, the irreversible post-mortem degradation 2 of ancient DNA has so far limited its recovery—outside permafrost areas—to specimens that are not older than approximately 0.5 million years (Myr) 3 . By contrast, tandem mass spectrometry has enabled the sequencing of approximately 1.5-Myr-old collagen type I 4 , and suggested the presence of protein residues in fossils of the Cretaceous period 5 —although with limited phylogenetic use 6 . In the absence of molecular evidence, the speciation of several extinct species of the Early and Middle Pleistocene epoch remains contentious. Here we address the phylogenetic relationships of the Eurasian Rhinocerotidae of the Pleistocene epoch 7 – 9 , using the proteome of dental enamel from a Stephanorhinus tooth that is approximately 1.77-Myr old, recovered from the archaeological site of Dmanisi (South Caucasus, Georgia) 10 . Molecular phylogenetic analyses place this Stephanorhinus as a sister group to the clade formed by the woolly rhinoceros ( Coelodonta antiquitatis ) and Merck’s rhinoceros ( Stephanorhinus kirchbergensis ). We show that Coelodonta evolved from an early Stephanorhinus lineage, and that this latter genus includes at least two distinct evolutionary lines. The genus Stephanorhinus is therefore currently paraphyletic, and its systematic revision is needed. We demonstrate that sequencing the proteome of Early Pleistocene dental enamel overcomes the limitations of phylogenetic inference based on ancient collagen or DNA. Our approach also provides additional information about the sex and taxonomic assignment of other specimens from Dmanisi. Our findings reveal that proteomic investigation of ancient dental enamel—which is the hardest tissue in vertebrates 11 , and is highly abundant in the fossil record—can push the reconstruction of molecular evolution further back into the Early Pleistocene epoch, beyond the currently known limits of ancient DNA preservation. Palaeoproteomic analysis of dental enamel from an Early Pleistocene Stephanorhinus resolves the phylogeny of Eurasian Rhinocerotidae, by enabling the reconstruction of molecular evolution beyond the limits of ancient DNA preservation.

Background Combining genomic data sets from multiple studies is advantageous to increase statistical power in studies where logistical considerations restrict sample size or require the sequential generation of data. However, significant technical heterogeneity is commonly observed across multiple batches of data that are generated from different processing or reagent batches, experimenters, protocols, or profiling platforms. These so-called batch effects often confound true biological relationships in the data, reducing the power benefits of combining multiple batches, and may even lead to spurious results in some combined studies. Therefore there is significant need for effective methods and software tools that account for batch effects in high-throughput genomic studies. Results Here we contribute multiple methods and software tools for improved combination and analysis of data from multiple batches. In particular, we provide batch effect solutions for cases where the severity of the batch effects is not extreme, and for cases where one high-quality batch can serve as a reference, such as the training set in a biomarker study. We illustrate our approaches and software in both simulated and real data scenarios. Conclusions We demonstrate the value of these new contributions compared to currently established approaches in the specified batch correction situations.

We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near ABO, ZC3HC1, and IGF2R. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer – but not other cancers – explain the most variance. Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter ). Ageing happens to us all, and as the cabaret singer Maurice Chevalier pointed out, \"old age is not that bad when you consider the alternative\". Yet, the growing ageing population of most developed countries presents challenges to healthcare systems and government finances. For many older people, long periods of ill health are part of the end of life, and so a better understanding of ageing could offer the opportunity to prolong healthy living into old age. Ageing is complex and takes a long time to study – a lifetime in fact. This makes it difficult to discern its causes, among the countless possibilities based on an individual’s genes, behaviour or environment. While thousands of regions in an individual’s genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle. Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average. The DNA of over 500,000 people was read to reveal the specific ‘genetic fingerprints’ of each participant. Then, after asking each of the participants how long both of their parents had lived, Timmers et al. pinpointed 12 DNA regions that affect lifespan. Five of these regions were new and had not been linked to lifespan before. Across the twelve as a whole several were known to be involved in Alzheimer’s disease, smoking-related cancer or heart disease. Looking at the entire genome, Timmers et al. could then predict a lifespan score for each individual, and when they sorted participants into ten groups based on these scores they found that top group lived five years longer than the bottom, on average. Many factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone. Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases. If such genes exist, their effects were too small to be detected in this study. The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease.

Background Osteoporosis affects mostly postmenopausal women, leading to deterioration of the microarchitectural bone structure and low bone mass, with an increased fracture risk with associated disability, morbidity and mortality. This Bayesian network meta-analysis compared the effects of current anti-osteoporosis drugs on bone mineral density. Methods The present systematic review and network meta-analysis follows the PRISMA extension statement to report systematic reviews incorporating network meta-analyses of health care interventions. The literature search was performed in June 2021. All randomised clinical trials that have investigated the effects of two or more drug treatments on BMD for postmenopausal osteoporosis were accessed. The network comparisons were performed through the STATA Software/MP routine for Bayesian hierarchical random-effects model analysis. The inverse variance method with standardised mean difference (SMD) was used for analysis. Results Data from 64 RCTs involving 82,732 patients were retrieved. The mean follow-up was 29.7 ± 19.6 months. Denosumab resulted in a higher spine BMD (SMD −0.220; SE 3.379), followed by pamidronate (SMD −5.662; SE 2.635) and zoledronate (SMD −10.701; SE 2.871). Denosumab resulted in a higher hip BMD (SMD −0.256; SE 3.184), followed by alendronate (SMD −17.032; SE 3.191) and ibandronate (SMD −17.250; SE 2.264). Denosumab resulted in a higher femur BMD (SMD 0.097; SE 2.091), followed by alendronate (SMD −16.030; SE 1.702) and ibandronate (SMD −17.000; SE 1.679). Conclusion Denosumab results in higher spine BMD in selected women with postmenopausal osteoporosis. Denosumab had the highest influence on hip and femur BMD. Level of evidence Level I, Bayesian network meta-analysis of RCTs

Randomized controlled trials are one of the best ways of quantifying the effectiveness of medical interventions. Therefore, when the authors of a randomized superiority trial report that differences in the primary outcome between the intervention group and the control group are “significant” (i.e., P ≤ 0.05), we might assume that the intervention has an effect on the outcome. Similarly, when differences between the groups are “not significant,” we might assume that the intervention does not have an effect on the outcome. Nevertheless, both assumptions are frequently incorrect.In this article, we explore the relationship that exists between real treatment effects and declarations of statistical significance based on P values and confidence intervals. We explain why, in some circumstances, the chance an intervention is ineffective when P ≤ 0.05 exceeds 25% and the chance an intervention is effective when P > 0.05 exceeds 50%.Over the last decade, there has been increasing interest in Bayesian methods as an alternative to frequentist hypothesis testing. We provide a robust but nontechnical introduction to Bayesian inference and explain why a Bayesian posterior distribution overcomes many of the problems associated with frequentist hypothesis testing.Notwithstanding the current interest in Bayesian methods, frequentist hypothesis testing remains the default method for statistical inference in medical research. Therefore, we propose an interim solution to the “significance problem” based on simplified Bayesian metrics (e.g., Bayes factor, false positive risk) that can be reported along with traditional P values and confidence intervals. We calculate these metrics for four well-known multicentre trials. We provide links to online calculators so readers can easily estimate these metrics for published trials. In this way, we hope decisions on incorporating the results of randomized trials into clinical practice can be enhanced, minimizing the chance that useful treatments are discarded or that ineffective treatments are adopted.

As alternatives to the normal distributions, t distributions are widely applied in robust analysis for data with outliers or heavy tails. The properties of the multivariate t distribution are well documented in Kotz and Nadarajah's book, which, however, states a wrong conclusion about the conditional distribution of the multivariate t distribution. Previous literature has recognized that the conditional distribution of the multivariate t distribution also follows the multivariate t distribution. We provide an intuitive proof without directly manipulating the complicated density function of the multivariate t distribution.

This study aimed to propose a novel method for dynamic risk assessment using a Bayesian network (BN) based on fuzzy data to decrease uncertainty compared to traditional methods by integrating Interval Type-2 Fuzzy Sets (IT2FS) and Z-numbers. A bow-tie diagram was constructed by employing the System Hazard Identification, Prediction, and Prevention (SHIPP) approach, the Top Event Fault Tree, and the Barriers Failure Fault Tree. The experts then provided their opinions and confidence levels on the prior probabilities of the basic events, which were then quantified utilizing the IT2FS and combined using the Z-number to reduce the uncertainty of the prior probability. The posterior probability of the critical basic events (CBEs) was obtained using the beta distribution based on recorded data on their requirements and failure rates over five years. This information was then fed into the BN. Updating the BN allowed calculating the posterior probability of barrier failure and consequences. Spherical tanks were used as a case study to demonstrate and confirm the significant benefits of the methodology. The results indicated that the overall posterior probability of Consequences after the failure probability of barriers displayed an upward trend over the 5-year period. This rise in IT2FS-Z calculation outcomes exhibited a shallower slope compared to the IT2FS mode, attributed to the impact of experts’ confidence levels in the IT2FS-Z mode. These differences became more evident by considering the 10 −4 variance compared to the 10 −5 . This study offers industry managers a more comprehensive and reliable understanding of achieving the most effective accident prevention performance.