Evidence-based medicine (EBM) aims for the ideal that healthcare professionals make conscientious, explicit, and judicious use of the best available evidence gained from the scientific method to clinical decision-making. It seeks to assess the strength of the evidence for benefits of diagnostic tests and treatments, using techniques from science, engineering, and statistics, such as the systematic review of medical literature, meta-analysis, risk-benefit analysis, and randomized controlled trials. The limited success rate of EBM therapies suggests that the complex nature of hair loss may be inadequately served by the present levels of evidence, and that physicians treating hair loss may have fallen short of adequately researching a robust evidence to underpin their practices. Against this backdrop, the concept of precision medicine (PM) is evolving. PM refers to the customization of medical care to the patient's individual characteristics based on the patient's genetic background and other molecular or cellular analysis, while classifying patients into subpopulations that differ in their susceptibility to a particular medical condition, in the biology or prognosis of those medical conditions, or in their response to a specific treatment. With the advances in hair research, the powerful tools of molecular biology and genetics, and innovative technologies, we have the robust scientific data and tools to adapt the concept of PM to the practice of trichiatry. Finally, databases pertaining to the development and efficacy of PM must be analyzed and be used to form the basis of evidence-based personalized trichiatry.
Department of Biomedical Engineering, Department of Electrical and Computer Engineering, Photonics Center, Boston University, Boston, Massachusetts, USA.
Background: Adaptative desaturation in fatty acid (FA) is an emerging hallmark of cancer metabolic plasticity. Desaturases such as stearoyl-CoA desaturase (SCD) and fatty acid desaturase 2 (FADS2) have been implicated in multiple cancers, and their dominant and compensatory effects have recently been highlighted. However, how tumors initiate and sustain their self-sufficient FA desaturation to maintain phenotypic transition remains elusive.
Background: The utilization of PD1 and CTLA4 inhibitors has revolutionized the treatment of malignant melanoma (MM). However, resistance to targeted and immune-checkpoint-based therapies still poses a significant problem.
Objective: Here, we mine large-scale MM proteogenomic data to identify druggable targets and forecast treatment efficacy and resistance.
Introduction: Low back pain (LBP) is a significant global health burden, with variable treatment outcomes and an unclear underlying molecular mechanism. Effective prediction of treatment responses remains a challenge. In this study, we aimed to develop gene signature-based machine learning models using transcriptomic data from peripheral immune cells to predict treatment outcomes in patients with LBP.
Background: Sepsis is the result of a dysregulated immune response to infection and is associated with acute organ dysfunction. The syndrome's complexity is contingent upon the underlying pathology and individual patient characteristics, including their immune response. The involvement of multiple organs and physiological functions adds complexity, with "organ cross-talk" emerging as a pivotal pathophysiological and clinical aspect.
Objectives: The enormous burden that cardiovascular diseases put on individuals and societies warrants reliable biomarkers of disease risk to optimize disease prevention. We studied longitudinal movement (LMov) in arterial walls using ultrasound of the common carotid artery (CCA). We believe that LMov could be a sensitive biomarker of cardiovascular health and in this study, we evaluate the intra-observer repeatability and inter-observer precision of our method.
