SummaryAim: To determine the prevalence of computer vision syndrome (CVS) and ergonomic practices among students in the Faculty of Medical Sciences at The University of the West Indies (UWI), Jamaica. Method:A cross-sectional study was done with a self-administered questionnaire.Results: Four hundred and nine students participated; 78% were females. The mean age was 21.6 years. Neck pain (75.1%), eye strain (67%), shoulder pain (65.5%) and eye burn (61.9%) were the most common CVS symptoms. Dry eyes (26.2%), double vision (28.9%) and blurred vision (51.6%) were the least commonly experienced symptoms.Eye burning (P = .001), eye strain (P = .041) and neck pain (P = .023) were significantly related to level of viewing. Moderate eye burning (55.1%) and double vision (56%) occurred in those who used handheld devices (P = .001 and .007, respectively). Moderate blurred vision was reported in 52% who looked down at the device compared with 14.8% who held it at an angle. Severe eye strain occurred in 63% of those who looked down at a device compared with 21% who kept the device at eye level. Shoulder pain was not related to pattern of use. Conclusion:Ocular symptoms and neck pain were less likely if the device was held just below eye level. There is a high prevalence of Symptoms of CVS amongst university students which could be reduced, in particular neck pain and eye strain and burning, with improved ergonomic practices. | INTRODUCTIONElectronic devices, laptops, tablets, ipads and smartphones, are now an integral part of studying at universities. Smart phone use in education is rapidly developing because of Google, Wikipedia and medical related apps.1-3 Computer vision syndrome (CVS) is at risk of becoming a major public health issue. 4The American Optometric Association defines CVS as a complex of eye and vision problems related to near vision activities involving computer use. 5 The prevalence of CVS ranges from 64% to 90%amongst computer users, with nearly 60 million people affected globally. 6,7 The most frequently occurring health related problems among computer users are CVS, wrist, neck, shoulder and back pain, an over use syndrome resulting in ocular and musculoskeletal discomfort. 8-10Students, who are frequent computer users are at increased risk of CVS and poor posture.

Biomarkers are broadly classified as genomic, proteomic, or metabolomic. Molecular biology and oncology research studies on oral cancer biomarkers focus on identifying key biological molecules or markers that could be linked to cancer development, risk assessment, screening, recurrence prediction, indicating prognosis, indicating invasion/metastasis and monitoring therapeutic responses of cancer. Cluster of differentiation factor 34 is a salivary biomarker that can identify recurrence potential of oral squamous cell carcinoma (OSCC). Integrin α3 and integrin β4 are genomic biomarkers that are helpful in estimating the risk of regional and hematogenous dissemination of malignant oral squamous cells. Other examples are vascular endothelial growth factor, B-cell lymphoma-2, claudin 4, yes-associated protein 1 and MET proto-oncogene, and receptor tyrosine kinase, which are genomic biomarkers that are used to predict radio-resistance in OSCC tissue. The present article reviews the clinical application, methodologies and steps in developing candidate biomarkers, protocols in reporting, evaluating candidate biomarkers, and challenges in biomarker research with a focus OSCC.

Suicide remains a clear, present and increasing public health problem, despite being a potentially preventable tragedy. Its incidence is particularly high in people with overt or un(der)diagnosed psychiatric disorders. Objective and precise identification of individuals at risk, ways of monitoring response to treatments and novel preventive therapeutics need to be discovered, employed and widely deployed. We sought to investigate whether blood gene expression biomarkers for suicide (that is, a ‘liquid biopsy’ approach) can be identified that are more universal in nature, working across psychiatric diagnoses and genders, using larger cohorts than in previous studies. Such markers may reflect and/or be a proxy for the core biology of suicide. We were successful in this endeavor, using a comprehensive stepwise approach, leading to a wealth of findings. Steps 1, 2 and 3 were discovery, prioritization and validation for tracking suicidality, resulting in a Top Dozen list of candidate biomarkers comprising the top biomarkers from each step, as well as a larger list of 148 candidate biomarkers that survived Bonferroni correction in the validation step. Step 4 was testing the Top Dozen list and Bonferroni biomarker list for predictive ability for suicidal ideation (SI) and for future hospitalizations for suicidality in independent cohorts, leading to the identification of completely novel predictive biomarkers (such as CLN5 and AK2), as well as reinforcement of ours and others previous findings in the field (such as SLC4A4 and SKA2). Additionally, we examined whether subtypes of suicidality can be identified based on mental state at the time of high SI and identified four potential subtypes: high anxiety, low mood, combined and non-affective (psychotic). Such subtypes may delineate groups of individuals that are more homogenous in terms of suicidality biology and behavior. We also studied a more personalized approach, by psychiatric diagnosis and gender, with a focus on bipolar males, the highest risk group. Such a personalized approach may be more sensitive to gender differences and to the impact of psychiatric co-morbidities and medications. We compared testing the universal biomarkers in everybody versus testing by subtypes versus personalized by gender and diagnosis, and show that the subtype and personalized approaches permit enhanced precision of predictions for different universal biomarkers. In particular, LHFP appears to be a strong predictor for suicidality in males with depression. We also directly examined whether biomarkers discovered using male bipolars only are better predictors in a male bipolar independent cohort than universal biomarkers and show evidence for a possible advantage of personalization. We identified completely novel biomarkers (such as SPTBN1 and C7orf73), and reinforced previously known biomarkers (such as PTEN and SAT1). For diagnostic ability testing purposes, we also examined as predictors phenotypic measures as apps (for suicide risk (CFI-S, Convergent Functional Information for Suici...

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 within-subject 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. MFAP3, which had no prior evidence in the literature for involvement in pain, had the most robust empirical evidence from our discovery and validation steps, and was a strong predictor for pain in the independent cohorts, particularly in females and males with PTSD. Other biomarkers with best overall convergent functional evidence for involvement in pain were GNG7, CNTN1, LY9, CCDC144B, and GBP1. Some of the individual biomarkers identified are targets of existing drugs. Moreover, the biomarker gene expression signatures were used for bioinformatic drug repurposing analyses, yielding leads for possible new drug candidates such as SC-560 (an NSAID), and amoxapine (an antidepressant), as well as natural compounds such as pyridoxine (vitamin B6), cyanocobalamin (vitamin B12), and apigenin (a plant flavonoid). Our work may help mitigate the diagnostic and treatment dilemmas that have contributed to the current opioid epidemic.