Recent Advances and the Potential for Clinical Use of Autofluorescence Detection of Extra-Ophthalmic Tissues

The autofluorescence (AF) characteristics of endogenous fluorophores allow the label-free assessment and visualization of cells and tissues of the human body. While AF imaging (AFI) is well-established in ophthalmology, its clinical applications are steadily expanding to other disciplines. This review summarizes clinical advances of AF techniques published during the past decade. A systematic search of the MEDLINE database and Cochrane Library databases was performed to identify clinical AF studies in extra-ophthalmic tissues. In total, 1097 articles were identified, of which 113 from internal medicine, surgery, oral medicine, and dermatology were reviewed. While comparable technological standards exist in diabetology and cardiology, in all other disciplines, comparability between studies is limited due to the number of differing AF techniques and non-standardized imaging and data analysis. Clear evidence was found for skin AF as a surrogate for blood glucose homeostasis or cardiovascular risk grading. In thyroid surgery, foremost, less experienced surgeons may benefit from the AF-guided intraoperative separation of parathyroid from thyroid tissue. There is a growing interest in AF techniques in clinical disciplines, and promising advances have been made during the past decade. However, further research and development are mandatory to overcome the existing limitations and to maximize the clinical benefits

Falls prevention advice and visual feedback to those at risk of falling : study protocol for a pilot randomized controlled trial

Studies have shown that functional strength and balance exercises can reduce the risk of falling in older people if they are done on a regular basis. However, the repetitive nature of these exercises; combined with the inherent lack of feedback of progress may discourage seniors from exercising in the home, thereby rendering such an intervention ineffective. This study hypothesizes that the use of visual feedback and multimodal games will be more effective in encouraging adherence to home rehabilitation than standard care; thereby promoting independence and improving the quality of life in older adults at risk of falling

ESC core curriculum for the general cardiologist (2013)

[No abstract available

A genome-wide association study suggests an association of Chr8p21.3 (GFRA2) with diabetic neuropathic pain

BACKGROUND: Neuropathic pain, caused by a lesion or a disease affecting the somatosensory system, is one of the most common complications in diabetic patients. The purpose of this study is to identify genetic factors contributing to this type of pain in a general diabetic population. METHOD: We accessed the Genetics of Diabetes Audit and Research Tayside (GoDARTS) datasets that contain prescription information and monofilament test results for 9439 diabetic patients, among which 6927 diabetic individuals were genotyped by Affymetrix SNP6.0 or Illumina OmniExpress chips. Cases of neuropathic pain were defined as diabetic patients with a prescription history of at least one of five drugs specifically indicated for the treatment of neuropathic pain and in whom monofilament test result was positive for sensory neuropathy in at least one foot. Controls were individuals who did not have a record of receiving any opioid analgesics. Imputation of non‐genotyped SNPs was performed by IMPUTE2, with reference files from 1000 Genomes Phase I datasets. RESULTS: After data cleaning and relevant exclusions, imputed genotypes of 572 diabetic neuropathic pain cases and 2491 diabetic controls were used in the Fisher's exact test. We identified a cluster in the Chr8p21.3, next to GFRA2 with a lowest p‐value of 1.77 × 10(−7) at rs17428041. The narrow‐sense heritability of this phenotype was 11.00%. CONCLUSION: This genome‐wide association study on diabetic neuropathic pain suggests new evidence for the involvement of variants near GFRA2 with the disorder, which needs to be verified in an independent cohort and at the molecular level

Clinical proteomics for precision medicine: the bladder cancer case

Precision medicine can improve patient management by guiding therapeutic decision based on molecular characteristics. The concept has been extensively addressed through the application of –omics based approaches. Proteomics attract high interest, as proteins reflect a “real-time” dynamic molecular phenotype. Focusing on proteomics applications for personalized medicine, a literature search was conducted to cover: a) disease prevention, b) monitoring/ prediction of treatment response, c) stratification to guide intervention and d) identification of drug targets. The review indicates the potential of proteomics for personalized medicine by also highlighting multiple challenges to be addressed prior to actual implementation. In oncology, particularly bladder cancer, application of precision medicine appears especially promising. The high heterogeneity and recurrence rates together with the limited treatment options, suggests that earlier and more efficient intervention, continuous monitoring and the development of alternative therapies could be accomplished by applying proteomics-guided personalized approaches. This notion is backed by studies presenting biomarkers that are of value in patient stratification and prognosis, and by recent studies demonstrating the identification of promising therapeutic targets. Herein, we aim to present an approach whereby combining the knowledge on biomarkers and therapeutic targets in bladder cancer could serve as basis towards proteomics- guided personalized patient management

Tracing diagnosis trajectories over millions of patients reveal an unexpected risk in schizophrenia.

The identification of novel disease associations using big-data for patient care has had limited success. In this study, we created a longitudinal disease network of traced readmissions (disease trajectories), merging data from over 10.4 million inpatients through the Healthcare Cost and Utilization Project, which allowed the representation of disease progression mapping over 300 diseases. From these disease trajectories, we discovered an interesting association between schizophrenia and rhabdomyolysis, a rare muscle disease (incidence < 1E-04) (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95], P-value 9.54E-15). We validated this association by using independent electronic medical records from over 830,000 patients at the University of California, San Francisco (UCSF) medical center. A case review of 29 rhabdomyolysis incidents in schizophrenia patients at UCSF demonstrated that 62% are idiopathic, without the use of any drug known to lead to this adverse event, suggesting a warning to physicians to watch for this unexpected risk of schizophrenia. Large-scale analysis of disease trajectories can help physicians understand potential sequential events in their patients