This observation provides the rationale for immunomodulation as a new therapeutic tool to deliberately modify host susceptibility, control the host response and avoid severe disease. The power of innate immunity as an arbitrator of health and disease is also highly relevant for emerging pathogens, including the current COVID-19 pandemic.A Correction to this paper has been published https//doi.org/10.1038/s41551-021-00725-w.The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of tumour-derived signals at dilution factors as low as 1 in 10,000. For a total of 308 patients with surgery-resectable lung cancer and 261 age- and sex-matched non-cancer control individuals recruited from two hospitals, the assay detected 52-81% of the patients at disease stages IA to III with a specificity of 96% (95% confidence interval (CI) 93-98%). In a subgroup of 115 individuals, the assay identified, at 100% specificity (95% CI 91-100%), nearly twice as many patients with cancer as those identified by ultradeep mutation sequencing analysis. The low amounts of ctDNA permitted by machine-learning-aided deep methylation sequencing could provide advantages in cancer screening and the assessment of treatment efficacy.Regular screening for the early detection of common chronic diseases might benefit from the use of deep-learning approaches, particularly in resource-poor or remote settings. Here we show that deep-learning models can be used to identify chronic kidney disease and type 2 diabetes solely from fundus images or in combination with clinical metadata (age, sex, height, weight, body-mass index and blood pressure) with areas under the receiver operating characteristic curve of 0.85-0.93. The models were trained and validated with a total of 115,344 retinal fundus photographs from 57,672 patients and can also be used to predict estimated glomerulal filtration rates and blood-glucose levels, with mean absolute errors of 11.1-13.4 ml min-1 per 1.73 m2 and 0.65-1.1 mmol l-1, and to stratify patients according to disease-progression risk. We evaluated the generalizability of the models for the identification of chronic kidney disease and type 2 diabetes with population-based external validation cohorts and via a prospective study with fundus images captured with smartphones, and assessed the feasibility of predicting disease progression in a longitudinal cohort.Improving the survival of patients with osteosarcoma has long proved challenging, although the treatment of this disease is on the precipice of advancement. The increasing feasibility of molecular profiling together with the creation of both robust model systems and large, well-annotated tissue banks has led to an increased understanding of osteosarcoma biology. The historical invariability of survival outcomes and the limited number of agents known to be active in the treatment of this disease facilitate clinical trials designed to identify efficacious novel therapies using small cohorts of patients. In addition, trial designs will increasingly consider the genetic background of the tumour through biomarker-based patient selection, thereby enriching for clinical activity. Indeed, osteosarcoma cells are known to express a number of surface proteins that might be of therapeutic relevance, including B7-H3, GD2 and HER2, which can be targeted using antibody-drug conjugates and/or adoptive cell therapies. In addition, immune-checkpoint inhibition might augment the latter approach by helping to overcome the immunosuppressive tumour microenvironment. In this Review, we provide a brief overview of current osteosarcoma therapy before focusing on the biological insights from the molecular profiling and preclinical modelling studies that have opened new therapeutic opportunities in this disease.Mutations in the genes encoding the cytoplasmic and mitochondrial forms of isocitrate dehydrogenase (IDH1 and IDH2, respectively; collectively referred to as IDH) are frequently detected in cancers of various origins, including but not limited to acute myeloid leukaemia (20%), cholangiocarcinoma (20%), chondrosarcoma (80%) and glioma (80%). In all cases, neomorphic activity of the mutated enzyme leads to production of the oncometabolite D-2-hydroxyglutarate, which has profound cell-autonomous and non-cell-autonomous effects. The broad effects of IDH mutations on epigenetic, differentiation and metabolic programmes, together with their high prevalence across a variety of cancer types, early presence in tumorigenesis and uniform expression in tumour cells, make mutant IDH an ideal therapeutic target. Herein, we describe the current biological understanding of IDH mutations and the roles of mutant IDH in the various associated cancers. We also present the available preclinical and clinical data on various methods of targeting IDH-mutant cancers and discuss, based on the underlying pathogenesis of different IDH-mutated cancer types, whether the treatment approaches will converge or be context dependent.Despite the known contributions of genes, genetic-guided pharmacotherapy has not been routinely implemented for venous thromboembolism (VTE). To examine evidence on cost-effectiveness of genetic-guided pharmacotherapy for VTE, we searched six databases, websites of four HTA agencies and citations, with independent double-reviewers in screening, data extraction, and quality rating. The ten eligible studies, all model-based, examined heterogeneous interventions and comparators. Findings varied widely; testing was cost-saving in two base-cases, cost-effective in four, not cost-effective in three, dominated in one. Of 22 model variables that changed decisions about cost-effectiveness, effectiveness/relative effectiveness of the intervention was the most frequent, albeit of poor quality. check details Studies consistently lacked details on the provision of interventions and comparators as well as on model development and validation. Besides improving the reporting of interventions, comparators, and methodological details, future economic evaluations should examine strategies recommended in guidelines and testing key model variables for decision uncertainty, to advise clinical implementations.