The electronic health record is a key component of healthcare information systems. Currently, numerous hospitals have adopted electronic health records to replace paper-based records to document care processes and improve care quality. Integrating healthcare information system into traditional nursing daily operations requires time and effort for nurses to become familiarized with this new technology. In the stages of electronic health record implementation, smooth adoption can streamline clinical nursing activities. In order to explore the adoption process, a descriptive qualitative study design and focus group interviews were conducted 3 months after and 2 years after electronic health record system implementation (system aborted 1 year in between) in one hospital located in southern Taiwan. Content analysis was performed to analyze the interview data, and six main themes were derived, in the first stage: (1) liability, work stress, and anticipation for electronic health record; (2) slow network speed, user-unfriendly design for learning process; (3) insufficient information technology/organization support; on the second stage: (4) getting used to electronic health record and further system requirements, (5) benefits of electronic health record in time saving and documentation, (6) unrealistic information technology competence expectation and future use. It concluded that user-friendly design and support by informatics technology and manpower backup would facilitate this adoption process as well.

Stem cells are capable of self-renewal and differentiation into a wide range of cell types with multiple clinical and therapeutic applications. Stem cells are providing hope for many diseases that currently lack effective therapeutic methods, including stroke, amyotrophic lateral sclerosis, Alzheimer's disease, and Parkinson's disease. Embryonic stem (ES) cells were originally targeted for differentiation into functional dopamine neurons for cell therapy. Today, induced pluripotent stem (iPS) cells are being tested for such purposes as generating functional dopamine neurons and treating a rat model of Parkinson's disease. In addition, neural stem cell and mesenchymal stem cells are also being used in neurodegenerative disorder therapies for stroke and Parkinson's disease. Although stem cell therapy is still in its infancy, it will likely become a powerful tool for many diseases that currently do not have effective therapeutic approaches. In this article, we discuss current research on the potential application of neural stem cells, mesenchymal stem cells, ES cells, and iPS cells to neurodegenerative disorders.

Induced pluripotent stem (iPS) cells are considered as having the greatest potential for use in cell-based therapies. However, at least two hurdles remain: integrating viral transgenes and introducing the c-Myc and Klf4 oncogenes. In a previous study, fibroblasts were incapable of generating iPS cells in the absence of both oncogenes and viral infection. For the present study, we tested our hypothesis that iPS cells can be generated without oncogenes and viral infection under hypoxic conditions and used for cell therapies. By avoiding oncogenic factors and virus integration, this strategy would decrease the potential for cancer formation. According to our observations, the repeated transfection of two expression plasmids (Oct4 and Sox2) into mouse embryonic fibroblasts (MEFs) and combined hypoxic condition resulted in the generation of a novel iPS cell. At 6 h post-transfection, MEFs were subjected to hypoxic conditions (3% O2) for 24 h; this procedure was repeated four times. The resulting MEFs were seeded on feeder cells on day 9; iPS cell clones were observed 12 days post-seeding and designated as iPS-OSH. Data for cell morphology, stem cell marker staining, gene expression profiles, and embryonic body, teratoma, and chimeric mouse formation indicated iPS-OSH pluripotent capability. Neural precursor cells differentiated from iPS-OSH cells were used to treat an ischemic stroke mouse model; results from a behavior analysis indicate that the therapeutic group surpassed the control group. Further, iPS-OSH-derived neural precursor cells differentiated into neurons and astrocytes in mouse stroke brains. In conclusion, we generated a novel iPS-OSH in the absence of viral infection and oncogenic factors and could use it for ischemic stroke therapy.

In practice, many medical domain datasets are incomplete, containing a proportion of incomplete data with missing attribute values. Missing value imputation can be performed to solve the problem of incomplete datasets. To impute missing values, some of the observed data (i.e., complete data) are generally used as the reference or training set, and then the relevant statistical and machine learning techniques are employed to produce estimations to replace the missing values. Since the collected dataset usually contains a certain number of feature dimensions, it is useful to perform feature selection for better pattern recognition. Therefore, the aim of this paper is to examine the effect of performing feature selection on missing value imputation of medical datasets. Experiments are carried out on five different medical domain datasets containing various feature dimensions. In addition, three different types of feature selection methods and imputation techniques are employed for comparison. The results show that combining feature selection and imputation is a better choice for many medical datasets. However, the feature selection algorithm should be carefully chosen in order to produce the best result. Particularly, the genetic algorithm and information gain models are suitable for lower dimensional datasets, whereas the decision tree model is a better choice for higher dimensional datasets.

Falls are one of the most common accidents among inpatients and may result in extended hospitalization and increased medical costs. Constructing a highly accurate fall prediction model could effectively reduce the rate of patient falls, further reducing unnecessary medical costs and patient injury. This study applied data mining techniques on a hospital's electronic medical records database comprising a nursing information system to construct inpatient-fall-prediction models for use during various stages of inpatient care. The inpatient data were collected from 15 inpatient wards. To develop timely and effective fall prediction models for inpatients, we retrieved the data of multiple-time assessment variables at four points during hospitalization. This study used various supervised machine learning algorithms to build classification models. Four supervised learning and two classifier ensemble techniques were selected for model development. The results indicated that Bagging+RF classifiers yielded optimal prediction performance at all four points during hospitalization. This study suggests that nursing personnel should be aware of patients' risk factors based on comprehensive fall risk assessment and provide patients with individualized fall prevention interventions to reduce inpatient fall rates.

Prosthodontic treatment has been a crucial part of dental treatment for patients with full mouth rehabilitation. Dental implant surgeries that replace conventional dentures using titanium fixtures have become the top choice. However, because of the wide-ranging scope of implant surgeries, patients' body conditions, surgeons' experience, and the choice of implant system should be considered during treatment. The higher price charged by dental implant treatments compared to conventional dentures has led to a rush among medical staff; therefore, the future impact of surgeries has not been analyzed in detail, resulting in medial disputes. Previous literature on the success factors of dental implants is mainly focused on single factors such as patients' systemic diseases, operation methods, or prosthesis types for statistical correlation significance analysis. This study developed a prediction model for providing an early warning mechanism to reduce the chances of dental implant failure. We collected the clinical data of patients who received artificial dental implants at the case hospital for a total of 8 categories and 20 variables. Supervised learning techniques such as decision tree (DT), support vector machines, logistic regressions, and classifier ensembles (i.e., Bagging and AdaBoost) were used to analyze the prediction of the failure of dental implants. The results show that DT with both Bagging and Adaboost techniques possesses the highest prediction performance for the failure of dental implant (area under the receiver operating characteristic curve, AUC: 0.741); the analysis also revealed that the implant systems affect dental implant failure. The model can help clinical surgeons to reduce medical failures by choosing the optimal implant system and prosthodontics treatments for their patients.

Vote by ballot is the feature in a democratic society and the process of decision-making, tending to achieve the philosophy of democratic politics by having the public who are eligible to vote for competent candidates or leaders. With the rapid development of technologies and network applications, electronization has been actively promoted globally during the social transformation period that the concept of electronic voting is further derived. The major advantages of electronic voting, comparing with traditional voting, lie in the mobility strength of electronic voting, reducing a large amount of election costs and enhancing the convenience for the public. Electronic voting allows voters completing voting on the Internet that not only are climate and location restrictions overcome, but the voter turnout is also increased and the voting time is reduced for the public. With the development in the past three decades, electronic voting presents outstanding performance theoretically and practically. Nevertheless, it is regrettable that electronic voting schemes still cannot be completely open because of lures by money and threats. People to lure by money and threats would confirm the voters following their instructions through various methods that more factors would appear on election results, affecting the quality and fairness of the election. In this study, this project aims to design an electronic voting scheme which could actually defend voters' free will so that lure of money and threats would fail. Furthermore, an electronic voting system based on Elliptic Curve Cryptography is proposed to ensure the efficiency and security, and Ring Signature and Signcryption are applied to reducing the computing costs. Moreover, this project also focuses on applying voting system to mobile devices. As the system efficiency and security are emphasized, voters do not need to participate in the election, but simply complete voting with smart phones, iPads, and computers. The votes would be automatically calculated and verified the results that the ballots are not necessarily printed, the printing of election mails is reduced, and manual handling is canceled. Such a method would effectively reduce voting costs and enhance the economic efficiency.

Embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs) are considered the most powerful in terms of differentiating into three-germ-layer cells. However, maintaining self-renewing ESCs and iPSCs in vitro requires leukemia-induced factor (LIF), an expensive reagent. Here we describe a less expensive compound that may serve as a LIF substitute-salvianolic acid B (Sal B), a Salvia miltiorrhiza extract. We found that Sal B is capable of upregulating Oct4 and Sox2, two genes considered important for the maintenance of ESC pluripotency. Our MTT data indicate that instead of triggering cell death, Sal B induced cell proliferation, especially at optimum concentrations of 0.01 nM and 0.1 nM. Other results indicate that compared to non-LIF controls, Sal B-treated ESCs expressed higher levels of several stem cell markers while still maintaining differentiation into three-germlayer cells after six passages. Further, we found that Sal B triggers the Jak2-Stat3 and EGFR-ERK1/2 signaling pathways. Following Sal B treatment, (a) levels of phosphorylated (p)-Jak2, p-Stat3, p-EGFR, and p-ERK proteins all increased; (b) these increases were suppressed by AG490 (a Jak2 inhibitor) and ZD1839 (an EGFR inhibitor); and (c) cytokines associated with the Jak2-Stat3 signaling pathway were upregulated. Our findings suggest that Sal B can be used as a LIF replacement for maintaining ESC pluripotency while increasing cell proliferation.