Tailoring of medical treatment: hemostasis and thrombosis towards precision medicine.
Ontology highlight
ABSTRACT:
SUBMITTER: Di Minno G
PROVIDER: S-EPMC5394976 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:ApreciseKUre is a multi-purpose digital platform facilitating data collection, integration and analysis for patients affected by Alkaptonuria (AKU), an ultra-rare autosomal recessive genetic disease. It includes genetic, biochemical, histopathological, clinical, therapeutic resources and quality of life scores that can be shared among registered researchers and clinicians in order to create a Precision Medicine Ecosystem (PME). The combination of machine learning application to analyse and re-interpret data available in the ApreciseKUre shows the potential direct benefits to achieve patient stratification and the consequent tailoring of care and treatments to a specific subgroup of patients. In this study, we have developed a tool able to investigate the most suitable treatment for AKU patients in accordance with their Quality of Life scores, which indicates changes in health status before/after the assumption of a specific class of drugs. This fact highlights the necessity of development of patient databases for rare diseases, like ApreciseKUre. We believe this is not limited to the study of AKU, but it represents a proof of principle study that could be applied to other rare diseases, allowing data management, analysis, and interpretation.
| S-EPMC7865235 | biostudies-literature
Project description:The traditional bench-to-bedside pipeline involves using model systems and patient samples to provide insights into pathways deregulated in cancer. This discovery reveals new biomarkers and therapeutic targets, ultimately stratifying patients and informing cohort-based treatment options. Precision medicine (molecular profiling of individual tumors combined with established clinical-pathological parameters) reveals, in real-time, individual patient's diagnostic and prognostic risk profile, informing tailored and tumor-specific treatment plans. Here we discuss advances in precision medicine presented at the Irish Association for Cancer Research Annual Meeting, highlighting examples where personalized medicine approaches have led to precision discovery in individual tumors, informing customized treatment programs.
| S-EPMC5704164 | biostudies-other
Project description:The pathogenesis of Cushing's disease is poorly understood; two recent reports identifying somatic mutations in USP8 in pituitary corticotroph tumors provide exciting advances in this field. These mutations alter EGFR trafficking and signaling, raising the prospect that EGFR inhibitors may move the treatment of this disease into the era of precision medicine.
| S-EPMC4456621 | biostudies-other
Project description:BackgroundThirty to seventy percent of all venous thromboembolism (VTE) events are associated with hospitalization. The absolute and relative risks during and after hospitalization are poorly characterized.ObjectivesQuantify the absolute rate and relative risk of VTE during and up to 3 months after medical and surgical hospitalizations.Patients/methodsWe conducted an observational cohort study between 2010 and 2016 of patients cared for by the University of Vermont (UVM) Health Network's primary care population. Cox proportional hazard models with hospitalization modeled as a time-varying covariate were used to estimate VTE risk.ResultsOver 4.3 years of follow-up, 55 220 hospitalizations (156 per 1000 person-years) and 713 first venous thromboembolism events (2.0 per 1000 person-years) occurred. Among individuals not recently hospitalized, the rate of venous thromboembolism was 1.4 per 1000 person-years and 71.8 per 1000 person-years during hospitalization. During the first, second, and third months after discharge, the rates of venous thromboembolism were 35.1, 11.3, and 5.2 per 1000 person-years, respectively. Relative to those not recently hospitalized, the age- and sex-adjusted HRs of venous thromboembolism were 38.0 (95% CI 28.0, 51.5) during hospitalization, and 18.4 (95% CI 15.0, 22.6), 6.3 (95% CI 4.3, 9.0), and 3.0 (95% CI 1.7, 5.4) during the first, second, and third months after discharge, respectively. Stratified by medical versus surgical services the rates were similar.ConclusionHospitalization and up to 3 months after discharge were strongly associated with increased venous thromboembolism risk. These data quantify this risk for use in future studies.
| S-EPMC9247009 | biostudies-literature
Project description: Not available
| S-EPMC8114084 | biostudies-literature
Project description: Not available
| S-EPMC7607071 | biostudies-literature
Project description:We endeavored to identify objective blood biomarkers for pain, a subjective sensation with a biological basis, using a stepwise discovery, prioritization, validation, and testing in independent cohorts design. We studied psychiatric patients, a high risk group for co-morbid pain disorders and increased perception of pain. For discovery, we used a powerful withinsubject longitudinal design. We were successful in identifying blood gene expression biomarkers that were predictive of pain state, and of future emergency department (ED) visits for pain, more so when personalized by gender and diagnosis.
2019-02-27 | GSE127208 | GEO
Project description:Obesity is a complex, multifactorial and chronic disease. Bariatric surgery is a safe and effective treatment intervention for obesity and obesity-related diseases. However, weight loss after surgery can be highly heterogeneous and is not entirely predictable, particularly in the long-term after intervention. In this review, we present and discuss the available data on patient-related and procedure-related factors that were previously appointed as putative predictors of bariatric surgery outcomes. In addition, we present a critical appraisal of the available evidence on which factors could be taken into account when recommending and deciding which bariatric procedure to perform. Several patient-related features were identified as having a potential impact on weight loss after bariatric surgery, including age, gender, anthropometrics, obesity co-morbidities, eating behavior, genetic background, circulating biomarkers (microRNAs, metabolites and hormones), psychological and socioeconomic factors. However, none of these factors are sufficiently robust to be used as predictive factors. Overall, there is no doubt that before we long for precision medicine, there is the unmet need for a better understanding of the socio-biological drivers of weight gain, weight loss failure and weight-regain after bariatric interventions. Machine learning models targeting preoperative factors and effectiveness measurements of specific bariatric surgery interventions, would enable a more precise identification of the causal links between determinants of weight gain and weight loss. Artificial intelligence algorithms to be used in clinical practice to predict the response to bariatric surgery interventions could then be created, which would ultimately allow to move forward into precision medicine in bariatric surgery prescription.
| S-EPMC10492755 | biostudies-literature
Project description:Tuberous sclerosis complex (TSC) is a rare genetic disorder caused by mutations in the TSC1 or TSC2 genes, which encode proteins that antagonise the mammalian isoform of the target of rapamycin complex 1 (mTORC1) - a key mediator of cell growth and metabolism. TSC is characterised by the development of benign tumours in multiple organs, together with neurological manifestations including epilepsy and TSC-associated neuropsychiatric disorders (TAND). Epilepsy occurs frequently and is associated with significant morbidity and mortality; however, the management is challenging due to the intractable nature of the seizures. Preventative epilepsy treatment is a key aim, especially as patients with epilepsy may be at a higher risk of developing severe cognitive and behavioural impairment. Vigabatrin given preventatively reduces the risk and severity of epilepsy although the benefits for TAND are inconclusive. These promising results could pave the way for evaluating other treatments in a preventative capacity, especially those that may address the underlying pathophysiology of TSC, including everolimus, cannabidiol and the ketogenic diet (KD). Everolimus is an mTOR inhibitor approved for the adjunctive treatment of refractory TSC-associated seizures that has demonstrated significant reductions in seizure frequency compared with placebo, improvements that were sustained after 2 years of treatment. Highly purified cannabidiol, recently approved in the US as Epidiolex® for TSC-associated seizures in patients ⩾1 years of age, and the KD, may also participate in the regulation of the mTOR pathway. This review focusses on the pivotal clinical evidence surrounding these potential targeted therapies that may form the foundation of precision medicine for TSC-associated epilepsy, as well as other current treatments including anti-seizure drugs, vagus nerve stimulation and surgery. New future therapies are also discussed, together with the potential for preventative treatment with targeted therapies. Due to advances in understanding the molecular genetics and pathophysiology, TSC represents a prototypic clinical syndrome for studying epileptogenesis and the impact of precision medicine.
| S-EPMC8290505 | biostudies-literature
Project description:The coronavirus disease 2019 (COVID-19) pandemic had a devastating impact on human society. Beginning with genome surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the development of omics technologies brought a clearer understanding of the complex SARS-CoV-2 and COVID-19. Here, we reviewed how omics, including genomics, proteomics, single-cell multi-omics, and clinical phenomics, play roles in answering biological and clinical questions about COVID-19. Large-scale sequencing and advanced analysis methods facilitate COVID-19 discovery from virus evolution and severity risk prediction to potential treatment identification. Omics would indicate precise and globalized prevention and medicine for the COVID-19 pandemic under the utilization of big data capability and phenotypes refinement. Furthermore, decoding the evolution rule of SARS-CoV-2 by deep learning models is promising to forecast new variants and achieve more precise data to predict future pandemics and prevent them on time.
| S-EPMC9846903 | biostudies-literature