Clinical and quality of life data correlation with a single-optic accommodating intraocular lens

Purpose: To examine a single-optic accommodating intraocular lens (IOL) visual performance by correlating IOL implanted eyes’ defocus curve with the intraocular aberrometric profile and the impact on the quality of life (QOL). Methods: Prospective consecutive case series study including a total of 25 eyes of 14 patients with ages ranging between 52 and 79 years old. All cases underwent cataract surgery with implantation of the single-optic accommodating IOL Crystalens HD (Bausch & Lomb). Distance and near visual acuity outcomes, intraocular aberrations, the defocus curve and QOL (NEI VFQ-25) were evaluated 3 months after surgery. Results: A significant improvement in distance visual acuity was found postoperatively (p = 0.02). Mean postoperative LogMAR uncorrected near visual acuity was 0.44 ± 0.23 (20/30). 60% of eyes had a postoperative addition between 0 and 1.5 diopters (D). The defocus curve showed an area of maximum visual acuity for the levels of defocus corresponding to distance and intermediate vision (−1 to +0.5 D). Postoperative intermediate visual acuity correlated significantly some QOL indices (r ≥ 0.51, p ≤ 0.03; difficulty in going down steps or seeing how people react to things that patient says) as well as with J0 component of manifest cylinder. Postoperative distance-corrected near visual acuity correlated significantly with age (r = 0.65, p < 0.01). Conclusions: This accommodating IOL seems to be able to restore the distance visual function as well as to provide an improvement in intermediate and near vision with a significant impact on patient's QOL, although limited by age and astigmatism. Future studies with larger sample sizes should confirm all these trends.

Satisfaction of clinical nurses with the formation of a university tutorial program

Degree in nursing from the Universitat Jaume I (UJI) maintains the continuity of learning with an integrated learning methodology (theory, simulated practice and clinical practice). The objective of this methodology is to achieve consistency between the knowledge, abilities and skills acquired in the classroom, laboratory and clinic to ensure skills related. Reference Nurse is a key figure in this process, you receive accredited training on Educational Methods, assessment of competence, and Evidence-Based Practice that plays the role of evaluating in conjunction with the subjects. It does not perceive economic remuneration. The main objective of this study is to determine the level of satisfaction of clinical nurses on the Nurses Training Program Reference in UJI (Castellon- Spain). A cross sectional study was performed and conducted on 150 nurses. 112 questionnaires were completed, collected and analysed at the end of training. The survey consists of 12 items measured with the Likert scale with 5 levels of response and two open questions regarding the positive and negative aspects of the course and to add in this formation. The training is always performed by the same faculty and it's used four sessions of 2012. We perform a quantitative analysis of the variables under study using measures of central tendency. The completion rate of the survey is 95.53% (n=107). Anonymity rate of 54,14% The overall satisfaction level of training was 3.65 (SD = 0.89) on 5 points. 54.2% (n = 58) of the reference nurses made a contribution in the open questions described in the overall results. The overall satisfaction level can be considered acceptable. It is considered necessary to elaborate a specific survey to detect areas of improvement of nurse training program reference and future recruitment strategies. The main objective of the present work is the selection and integration of different methodologies among those applicable within the framework of the European Higher Education Area to combine teaching methods with high implication from both lecturers and students.

Improvement of Virtual Screening Predictions using Computational Intelligence Methods

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of scoring functions used in most VS methods we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, this information being exploited afterwards to improve VS predictions.