Basic fibroblast growth factor promotes melanocyte migration via activating PI3K/Akt-Rac1-FAK-JNK and ERK signaling pathways.

Vitiligo is a depigmentation disorder characterized by loss of functional melanocytes of the skin epidermis. The pathogenesis of vitiligo remains elusive. The purpose of this study is to investigate the effects of basic fibroblast growth factor (bFGF) on melanocyte migration, including its biochemical mechanism using transwell assay in vitro. We found that melanocyte treated with bFGF showed a significant increase in migration and cytoskeletal rearrangement. These changes were associated with increased activation of PI3K/Akt, Rac1, FAK, JNK, and ERK. Likewise, reduction of PI3K/Akt, Rac1, FAK, JNK, and ERK activity using selective inhibitors or siRNA was associated with impediment of bFGF-induced melanocyte migration. In addition, activity of Rac1, FAK, and JNK was reduced in cells in which PI3K/Akt was inhibited, activity of FAK and JNK was reduced in cells in which the Rac1 was inhibited, and activity of JNK was reduced in cells in which the FAK was inhibited. Collectively, these data demonstrate that bFGF facilitated melanocyte migration via PI3K/Akt-Rac1-FAK-JNK and ERK signaling pathways. © 2016 IUBMB Life, 68(9):735-747, 2016.

Prospective stratification of patients at risk for emergency department revisit: resource utilization and population management strategy implications.

BACKGROUND: Estimating patient risk of future emergency department (ED) revisits can guide the allocation of resources, e.g. local primary care and/or specialty, to better manage ED high utilization patient populations and thereby improve patient life qualities. METHODS: We set to develop and validate a method to estimate patient ED revisit risk in the subsequent 6 months from an ED discharge date. An ensemble decision-tree-based model with Electronic Medical Record (EMR) encounter data from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), was developed and validated, assessing patient risk for a subsequent 6 month return ED visit based on the ED encounter-associated demographic and EMR clinical history data. A retrospective cohort of 293,461 ED encounters that occurred between January 1, 2012 and December 31, 2012, was assembled with the associated patients' 1-year clinical histories before the ED discharge date, for model training and calibration purposes. To validate, a prospective cohort of 193,886 ED encounters that occurred between January 1, 2013 and June 30, 2013 was constructed. RESULTS: Statistical learning that was utilized to construct the prediction model identified 152 variables that included the following data domains: demographics groups (12), different encounter history (104), care facilities (12), primary and secondary diagnoses (10), primary and secondary procedures (2), chronic disease condition (1), laboratory test results (2), and outpatient prescription medications (9). The c-statistics for the retrospective and prospective cohorts were 0.742 and 0.730 respectively. Total medical expense and ED utilization by risk score 6 months after the discharge were analyzed. Cluster analysis identified discrete subpopulations of high-risk patients with distinctive resource utilization patterns, suggesting the need for diversified care management strategies. CONCLUSIONS: Integration of our method into the HIN secure statewide data system in real time prospectively validated its performance. It promises to provide increased opportunity for high ED utilization identification, and optimized resource and population management.

NLP based congestive heart failure case finding: A prospective analysis on statewide electronic medical records.

BACKGROUND: In order to proactively manage congestive heart failure (CHF) patients, an effective CHF case finding algorithm is required to process both structured and unstructured electronic medical records (EMR) to allow complementary and cost-efficient identification of CHF patients. METHODS AND RESULTS: We set to identify CHF cases from both EMR codified and natural language processing (NLP) found cases. Using narrative clinical notes from all Maine Health Information Exchange (HIE) patients, the NLP case finding algorithm was retrospectively (July 1, 2012-June 30, 2013) developed with a random subset of HIE associated facilities, and blind-tested with the remaining facilities. The NLP based method was integrated into a live HIE population exploration system and validated prospectively (July 1, 2013-June 30, 2014). Total of 18,295 codified CHF patients were included in Maine HIE. Among the 253,803 subjects without CHF codings, our case finding algorithm prospectively identified 2411 uncodified CHF cases. The positive predictive value (PPV) is 0.914, and 70.1% of these 2411 cases were found to be with CHF histories in the clinical notes. CONCLUSIONS: A CHF case finding algorithm was developed, tested and prospectively validated. The successful integration of the CHF case findings algorithm into the Maine HIE live system is expected to improve the Maine CHF care.

Pilot Application of Magnetic Nanoparticle-Based Biosensor for Necrotizing Enterocolitis.

BACKGROUND: Necrotizing Enterocolitis (NEC) is a major source of neonatal morbidity and mortality. There is an ongoing need for a sensitive diagnostic instrument to discriminate NEC from neonatal sepsis. We hypothesized that magnetic nanopartile-based biosensor analysis of gut injury-associated biomarkers would provide such an instrument. STUDY DESIGN: We designed a magnetic multiplexed biosensor platform, allowing the parallel plasma analysis of C-reactive protein (CRP), matrix metalloproteinase-7 (MMp7), and epithelial cell adhesion molecule (EpCAM). Neonatal subjects with sepsis (n=5) or NEC (n=10) were compared to control (n=5) subjects to perform a proof of concept pilot study for the diagnosis of NEC using our ultra-sensitive biosensor platform. RESULTS: Our multiplexed NEC magnetic nanoparticle-based biosensor platform was robust, ultrasensitive (Limit of detection LOD: CRP 0.6 pg/ml; MMp7 20 pg/ml; and EpCAM 20 pg/ml), and displayed no cross-reactivity among analyte reporting regents. To gauge the diagnostic performance, bootstrapping procedure (500 runs) was applied: MMp7 and EpCAM collectively differentiated infants with NEC from control infants with ROC AUC of 0.96, and infants with NEC from those with sepsis with ROC AUC of 1.00. The 3-marker panel comprising of EpCAM, MMp7 and CRP had a corresponding ROC AUC of 0.956 and 0.975, respectively. CONCLUSION: The exploration of the multiplexed nano-biosensor platform shows promise to deliver an ultrasensitive instrument for the diagnosis of NEC in the clinical setting.

Risk prediction of emergency department revisit 30 days post discharge: a prospective study.

BACKGROUND: Among patients who are discharged from the Emergency Department (ED), about 3% return within 30 days. Revisits can be related to the nature of the disease, medical errors, and/or inadequate diagnoses and treatment during their initial ED visit. Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization. METHODS AND FINDINGS: A decision tree based model with discriminant Electronic Medical Record (EMR) features was developed and validated, estimating patient ED 30 day revisit risk. A retrospective cohort of 293,461 ED encounters from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), between January 1, 2012 and December 31, 2012, was assembled with the associated patients' demographic information and one-year clinical histories before the discharge date as the inputs. To validate, a prospective cohort of 193,886 encounters between January 1, 2013 and June 30, 2013 was constructed. The c-statistics for the retrospective and prospective predictions were 0.710 and 0.704 respectively. Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score. Cluster analysis of high-risk patients identified discrete sub-populations with distinctive demographic, clinical and resource utilization patterns. CONCLUSIONS: Our ED 30-day revisit model was prospectively validated on the Maine State HIN secure statewide data system. Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes.

A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis.

BACKGROUND: Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. STUDY DESIGN: A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. RESULTS: Machine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. ALGORITHM AVAILABILITY: http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.

A novel therapeutic strategy using ultrasound mediated microbubbles destruction to treat colon cancer in a mouse model.

The goal of this study was to determine whether ultrasound mediated microbubbles destruction (UMMD) could inhibit colon cancer growth in a mouse model. Six-week-old balb/c female nude mice were subcutaneously inoculated with HT29-GFP cells (HT29 cells labeled with green fluorescent dye) in axilla to establish a xenograft mouse model of colon carcinoma, which were randomly divided into five groups (n=10 each): group A (blank group): no treatment; group B (saline only); group C (saline+ultrasound exposure); group D (intravenous microbubbles only); and group E (intravenous microbubbles+ultrasound exposure). Treatment of each group was performed on days 20, 21, and 22 after inoculation. Tumor growth and metastatic spread were monitored by the whole-body fluorescent imaging, tumor volume growth and body weight growth curve were obtained as well. The mice were euthanized 30 days after treatment. Specimens of the tumor tissues were evaluated pathologically using light microscopy and transmission electron microscopy. Necrosis percentages, microvascular density and tumor cells damage of each tumor were assessed histologically. Our data indicate that: (1) tumor growth in group E (intravenous microbubbles+ultrasound exposure) was significantly decreased after four weeks post inoculation, compared with other control treatments (P<0.05); (2) the tumor weight at sacrifice in group E was significantly lower than that in other groups; (3) The intravenous microbubbles combined with ultrasound exposure treated mice showed significantly decreased expression levels of CD31. (4)The pathological changes of absence of nucleus membrane, chromatin condensation, mitochondrial vacuolation and hemorrhagic damage of microvessel were observed in the tumors of group E only, whereas these changes occurred rarely in other groups; and (5) no metastatic lesion was found in any group throughout this study using whole-body fluorescent imaging, and the skin of the mouse in group E was intact after UMMD treatment. Our results suggest that UMMD can be used as a promising novel therapeutic strategy to treat colon cancer.