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In the present study, we investigated the effect of melatonin on the preimplantation development of porcine parthenogenetic and somatic cell nuclear transfer (SCNT) embryos. Parthenogenetic embryos were cultured in mNCSU-23 supplemented with various concentrations of melatonin for 7 days. The results revealed that 100 pM was the optimal concentration, which resulted in significantly increased cleavage and blastocyst formation rates. Additionally, 100 pM melatonin provided the highest increase in total cell number of blastocysts. Therefore, the subsequent experiments were performed with 100 pM melatonin. ROS level in 2-8 cell stage embryos in the presence or absence of melatonin was evaluated. Embryos cultured with melatonin showed significantly decreased ROS. Blastocysts cultured with melatonin for 7 days were analyzed by the TUNEL assay. It was observed that melatonin not only increased (P < 0.05) the total cell number but also decreased (P < 0.05) the rate of apoptotic nuclei. Blastocysts cultured with melatonin were assessed for the expression of apoptosis-related genes Bcl-xl and Bax, and of pluripotency marker gene Oct-4 by real-time quantitative PCR. Analysis of data showed that the expression of Bcl-xl was higher (1.7-fold) compared to the control while the expression of Bax was significantly decreased relative to the control (0.7-fold) (P < 0.05). Moreover, the expression of Oct-4 was 1.7-fold higher than the control. These results indicated that melatonin had beneficial effects on the development of porcine parthenogenetic embryos. Based on the findings of parthenogenetic embryos, we investigated the effect of melatonin on the development of porcine SCNT embryos. The results also demonstrated increased cleavage and blastocyst formation rates, and the total cell numbers in blastocysts were significantly higher when the embryos were cultured with melatonin. Therefore, these data suggested that melatonin may have important implications for improving porcine preimplantation SCNT embryo development.

Background Patients in intensive care units are at higher risk for development of pressure ulcers than other patients. In order to prevent pressure ulcers from developing in intensive care patients, risk for development of pressure ulcers must be assessed accurately. Objectives To evaluate the predictive validity of the Braden scale for assessing risk for development of pressure ulcers in intensive care patients by using 4 years of data from electronic health records. Methods Data from the electronic health records of patients admitted to intensive care units between January 1, 2007, and December 31, 2010, were extracted from the data warehouse of an academic medical center. Predictive validity was measured by using sensitivity, specificity, positive predictive value, and negative predictive value. The receiver operating characteristic curve was generated, and the area under the curve was reported. Results A total of 7790 intensive care patients were included in the analysis. A cutoff score of 16 on the Braden scale had a sensitivity of 0.954, specificity of 0.207, positive predictive value of 0.114, and negative predictive value of 0.977. The area under the curve was 0.672 (95% CI, 0.663–0.683). The optimal cutoff for intensive care patients, determined from the receiver operating characteristic curve, was 13. Conclusions The Braden scale shows insufficient predictive validity and poor accuracy in discriminating intensive care patients at risk of pressure ulcers developing. The Braden scale may not sufficiently reflect characteristics of intensive care patients. Further research is needed to determine which possibly predictive factors are specific to intensive care units in order to increase the usefulness of the Braden scale for predicting pressure ulcers in intensive care patients.

A system for somatic cell nuclear transfer (SCNT) was developed and led to the successful production of GFP-transfected piglets. In experiment 1, two groups of SCNT couplets reconstructed with porcine fetal fibroblasts (PFF) and enucleated sow (S) or gilt oocytes (G): 1). received a simultaneous electrical fusion/activation (S-EFA or G-EFA groups), or 2). were electrically fused followed by activation with ionomycin (S-EFIA or G-EFIA groups), or 3). were subjected to electrical fusion and subsequent activation by ionomycin, followed by 6-dimethylaminopurine treatment (S-EFIAD or G-EFIAD groups). The frequency of blastocyst formation was significantly higher in S-EFA (26%) compared with that observed in the other experimental groups (P < 0.05), but not with S-EFIA (23%). Sow oocytes yielded significantly higher cleavage frequencies (68%-69%) and total cell numbers of blastocysts when compared with gilt oocytes, regardless of fusion/activation methods (P < 0.05). However, the ratio of inner cell mass (ICM)/total cells in G-EFA and S-EFA was significantly lower than in the other groups (P < 0.05). In experiment 2, SCNT couplets reconstructed with PFF cultured in the presence or absence of serum and enucleated sow oocytes were subjected to EFA. There were no effects of serum starvation on cell-cycle synchronization, developmental competence, total cell numbers, and ratio of ICM/total cells. In experiment 3, SCNT couplets reconstructed with PFF transfected with an enhanced green fluorescence protein (EGFP) gene using FuGENE-6 and enucleated sow oocytes were subjected to EFA and cultured for 7 days. Expression frequencies of GFP gene during development were 100%, 78%, 72%, 71%, and 70% in fused, two-cell, four to eight cells, morulae, and blastocysts, respectively. In experiment 4, SCNT embryos derived from different recipient cytoplasts (sows or gilts) and donor karyoplasts (PFF or GFP-transfected) were subjected to EFA and transferred to the oviducts of surrogates. The pregnancy rates in SCNT embryos derived from sow oocytes (66%-69%) were higher than those with gilt oocytes (23%-27%) regardless of donor cell types. One live offspring from GFP-SCNT embryos and two from PFF-SCNT embryos were delivered. Microsatellite analysis confirmed that the clones were genetically identical to the donor cells and polymerase chain reaction (PCR) from genomic DNA of cloned piglets and subsequent southern blot analysis confirmed the integration of EGFP gene into chromosomes.

BackgroundWe develop predictive models enabling clinicians to better understand and explore patient clinical data along with risk factors for pressure ulcers in intensive care unit patients from electronic health record data. Identifying accurate risk factors of pressure ulcers is essential to determining appropriate prevention strategies; in this work we examine medication, diagnosis, and traditional Braden pressure ulcer assessment scale measurements as patient features. In order to predict pressure ulcer incidence and better understand the structure of related risk factors, we construct Bayesian networks from patient features. Bayesian network nodes (features) and edges (conditional dependencies) are simplified with statistical network techniques. Upon reviewing a network visualization of our model, our clinician collaborators were able to identify strong relationships between risk factors widely recognized as associated with pressure ulcers.MethodsWe present a three-stage framework for predictive analysis of patient clinical data: 1) Developing electronic health record feature extraction functions with assistance of clinicians, 2) simplifying features, and 3) building Bayesian network predictive models. We evaluate all combinations of Bayesian network models from different search algorithms, scoring functions, prior structure initializations, and sets of features.ResultsFrom the EHRs of 7,717 ICU patients, we construct Bayesian network predictive models from 86 medication, diagnosis, and Braden scale features. Our model not only identifies known and suspected high PU risk factors, but also substantially increases sensitivity of the prediction - nearly three times higher comparing to logistical regression models - without sacrificing the overall accuracy. We visualize a representative model with which our clinician collaborators identify strong relationships between risk factors widely recognized as associated with pressure ulcers.ConclusionsGiven the strong adverse effect of pressure ulcers on patients and the high cost for treating pressure ulcers, our Bayesian network based model provides a novel framework for significantly improving the sensitivity of the prediction model. Thus, when the model is deployed in a clinical setting, the caregivers can suitably respond to conditions likely associated with pressure ulcer incidence.