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BACKGROUND: Patients' dependency has significant nursing implications. Nurse skill mix and staffing levels may be addressed more successfully when dependency can be measured. In the oncology emergency room, a valid and reliable tool that measures patients' dependency on nursing care is necessary. AIM: This study aimed to evaluate the psychometric properties of the Jones Dependency Tool in Adult Oncology Emergency Setting at a Cancer Center in Jordan. METHODS: A prospective cross-sectional design was used to test the Reliability and Validity of the Jones Dependency Tool among patients with cancer visiting the ED. A sample of 79 patients were assessed using the JDT and Conner's tool. RESULTS: Jones Dependency Tool showed a high level of validity and reliability. In terms of reliability, which was tested by test-re-test, Intra-class correlation (ICC) = 0.902 which indicates good to excellent. The tool demonstrates a high validity evidenced by its correlation with a criterion (p < 0.001). CONCLUSION: The study demonstrated that the JDT tool is a valid and reliable tool that can be used to quantify a patient's dependency level and the level of nursing care they need, assisting in the selection of the ideal staffing level in terms of quantity and skill mix.

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Background: In this investigation, we explore the literature regarding neuroregeneration from the 1700s to the present. The regeneration of central nervous system neurons or the regeneration of axons from cell bodies and their reconnection with other neurons remains a major hurdle. Injuries relating to war and accidents attracted medical professionals throughout early history to regenerate and reconnect nerves. Early literature till 1990 lacked specific molecular details and is likely provide some clues to conditions that promoted neuron and/or axon regeneration. This is an avenue for the application of natural language processing (NLP) to gain actionable intelligence. Post 1990 period saw an explosion of all molecular details. With the advent of genomic, transcriptomics, proteomics, and other omics-there is an emergence of big data sets and is another rich area for application of NLP. How the neuron and/or axon regeneration related keywords have changed over the years is a first step towards this endeavor. Methods: Specifically, this article curates over 600 published works in the field of neuroregeneration. We then apply a dynamic topic modeling algorithm based on the Latent Dirichlet allocation (LDA) algorithm to assess how topics cluster based on topics. Results: Based on how documents are assigned to topics, we then build a recommendation engine to assist researchers to access domain-specific literature based on how their search text matches to recommended document topics. The interface further includes interactive topic visualizations for researchers to understand how topics grow closer and further apart, and how intra-topic composition changes over time. Conclusions: We present a recommendation engine and interactive interface that enables dynamic topic modeling for neuronal regeneration.

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We present an overview of current state of proteomic approaches as applied to optic nerve regeneration in the historical context of nerve regeneration particularly central nervous system neuronal regeneration. We present outlook pertaining to the optic nerve regeneration proteomics that the latter can extrapolate information from multi-systems level investigations. We present an account of the current need of systems level standardization for comparison of proteome from various models and across different pharmacological or biophysical treatments that promote adult neuron regeneration. We briefly overview the need for deriving knowledge from proteomics and integrating with other omics to obtain greater biological insight into process of adult neuron regeneration in the optic nerve and its potential applicability to other central nervous system neuron regeneration.

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AIM: This research was undertaken with the aim of using personality traits, learning styles and handedness to develop and evaluate a scale to measure a new concept in operative dentistry, symmetrisation. An initial hypothesis was proposed, stating that handedness, personality trait and learning style could be used to predict symmetrisation potential in the domain of operative dentistry. METHODS: Participants completed three questionnaires: the Edinburgh handedness inventory assessment; the Keirsey Temperament Sorter-II (KTS-II); and the learning style assessment (VARK). Then, participants prepared a cavity on an artificial third molar tooth in the traditional phantom head using their non-dominant hand. For evaluation and comparison purposes, Standard Tessellation Language images were obtained from prepared teeth using a CAD/CAM scanner and then aligned against a reference "unprepared" tooth image using a custom software developed by the authors based on the Iterative Closest Point algorithm. RESULTS: Extroverts, intuitive and participants characterised as relying on their feelings were more accurate compared to introverts, sensory and toughminded participants, respectively. Extroverts were faster to complete the task compared to introverts. Participants with a preference for read/writing learning style produced more errors than any of the other groups. Handedness, however, did not show any significant association with performance. CONCLUSIONS: It was concluded that both the KTS-II and the learning style assessment (VARK) questionnaires possess potential as useful measures of symmetrisation potential and the ability to use the non-dominant hand in operative dentistry, as well as platform for continuing research.

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Growth cones (GCs) are structures associated with growing neurons. GC membrane expansion, which necessitates protein-lipid interactions, is critical to axonal elongation in development and in adult neuritogenesis. We present a multi-omic analysis that integrates proteomics and lipidomics data for the identification of GC pathways, cell phenotypes, and lipid-protein interactions, with an analytic platform to facilitate the visualization of these data. We combine lipidomic data from GC and adult axonal regeneration following optic nerve crush. Our results reveal significant molecular variability in GCs across developmental ages that aligns with the upregulation and downregulation of lipid metabolic processes and correlates with distinct changes in the lipid composition of GC plasmalemma. We find that these processes also define the transition into a growth-permissive state in the adult central nervous system. The insight derived from these analyses will aid in promoting adult regeneration and functional innervation in devastating neurodegenerative diseases.

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OBJECTIVE: Compared with medicine, less research has focused on adoption rates and factors contributing to the adoption of electronic dental records (EDRs) and certified electronic health records (EHRs) in the field of dentistry. We ran two multivariate models on EDR adoption and certification-capable EHR adoption to determine environmental and organizational factors associated with adoption. METHODS: We conducted telephone survey of a 10-item questionnaire using disproportionate stratified sampling procedure of 149 dental clinics in Tennessee in 2017 measuring adoption of dental information technology (IT) (EDRs and certification-capable EHRs) and practice characteristics, including region, rurality, specialty, and practice size. We used binomial logistic regression models to determine associations of adoption with predictor variables. RESULTS: A total of 77% of surveyed dental clinics in Tennessee had adopted some type of EDR system. When the definitions of certification capable were applied, the adoption rates in dental clinics dropped to 58%. A binomial logistic regression model for the effects of rurality, specialization, and practice size on the likelihood that a clinic would adopt an EHR product was statistically significant (chi-square (3) = 12.41, p = 0.0061). Of the three predictor variables, specialization and practice size were significant: Odds of adopting an EHR is 67% lower for specialists than for general dentists; and clinics with two or more practicing dentists were associated with a much greater likelihood of adopting an EHR-capable system (adjusted odds ratio = 3.09, p = 0.009). CONCLUSION: Findings from this study indicate moderate to high levels of overall dental IT adoption. However, adoption rates in dental clinics do remain lower than those observed in office-based physician practices in medicine. Specialization and practice size were significant predictors of EHR-capable system adoption. Efforts to increase EHR adoption in dentistry should be mindful of potential disparities in smaller practices and between dental specialties and generalists.

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