Optimization and comparison of non-conventional extraction technologies for Citrus paradisi L. peels: a valorization approach.

The phenolics from grapefruit peel were obtained by ultrasound assisted extraction (UAE) and enzyme-assisted extraction (EAE). Extraction parameters were optimized using response surface methodology to maximize the yield of total phenolic content (TPC) and total flavonoid content (TFC). The optimized extracts from UAE and EAE were then compared with conventional solvent extraction for their TPC, TFC, antioxidant activity and phenolic composition. The best optimized conditions for UAE was obtained at 33.12 min extraction time, 71.11% amplitude and a solvent-solid (SS) ratio of 39.63 mL/g. The optimized parameters for EAE were 4.81 h extraction time, 0.9% enzyme concentration and 40 mL/g SS ratio. Similar values of experimental and predicted TPC and TFC at optimized conditions indicates the suitability of the quadratic model in optimizing the extraction parameters. Further characterization of extracts suggested EAE as most efficient process in extracting bioactive compounds.

Impact of population management with direct physician feedback on care of patients with type 2 diabetes.

OBJECTIVE: Population-level strategies may improve primary care for diabetes. We designed a controlled study to assess the impact of population management versus usual care on metabolic risk factor testing and management in patients with type 2 diabetes. We also identified potential patient-related barriers to effective diabetes management. RESEARCH DESIGN AND METHODS: We used novel clinical software to rank 910 patients in a diabetes registry at a single primary care clinic and thereby identify the 149 patients with the highest HbA(1c) and cholesterol levels. After review of the medical records of these 149 patients, evidence-based guideline recommendations regarding metabolic testing and management were sent via e-mail to each intervention patient's primary care provider (PCP). Over a 3-month follow-up period, we assessed changes in the evidence-based management of intervention patients compared with a matched cohort of control patients receiving usual care at a second primary care clinic affiliated with the same academic medical center. RESULTS: In the intervention cohort, PCPs followed testing recommendations more often (78%) than therapeutic change recommendations (36%, P = 0.001). Compared with the usual care control cohort, population management resulted in a greater overall proportion of evidence-based guideline practices being followed (59 vs. 45%, P = 0.02). Most intervention patients (62%) had potential barriers to effective care, including depression (35%), substance abuse (26%), and prior nonadherence to care plans (18%). CONCLUSIONS: Population management with clinical recommendations sent to PCPs had a modest but statistically significant impact on the evidence-based management of diabetes compared with usual care. Depression and substance abuse are prevalent patient-level adherence barriers in patients with poor metabolic control.

A controlled trial of population management: diabetes mellitus: putting evidence into practice (DM-PEP).

OBJECTIVE: Population-level strategies to organize and deliver care may improve diabetes management. We conducted a multiclinic controlled trial of population management in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: We created diabetic patient registries (n = 3,079) for four primary care clinics within a single academic health center. In the intervention clinic (n = 898), a nurse practitioner used novel clinical software (PopMan) to identify patients on a weekly basis with outlying values for visit and testing intervals and last measured levels of HbA1c, LDL cholesterol, and blood pressure. For these patients, the nurse practitioner e-mailed a concise patient-specific summary of evidence-based management suggestions directly to primary care providers (PCPs). Population changes in risk factor testing, medication prescription, and risk factor levels from baseline (1 January 2000 to 31 August 2001) to follow-up (1 December 2001 to 31 July 2003) were compared with the three usual-care control clinics (n = 2,181). RESULTS: Patients had a mean age of 65 years, were mostly white (81%), and the majority were insured by Medicare/Medicaid (62%). From baseline to follow-up, the increase in proportion of patients tested for HbA1c (P = 0.004) and LDL cholesterol (P < 0.001) was greater in the intervention than control sites. Improvements in diabetes-related medication prescription and levels of HbA1c, LDL cholesterol, and blood pressure in the intervention clinic were balanced by similar improvements in the control sites. CONCLUSIONS: Population-level clinical registries combined with summarized recommendations to PCPs had a modest effect on management. The intervention was limited by good overall quality of care at baseline and temporal improvements in all control clinics. It is unknown whether this intervention would have had greater impact in clinical settings with lower overall quality. Further research into more effective methods of translating population registry information into action is required.

Multinucleation of a sibling blastomere on day 2 suggests unsuitability for embryo transfer in IVF-preimplantation genetic screening cycles.

OBJECTIVE: To evaluate the impact of multinucleation of a sibling blastomere of day 2 embryos on the rate of aneuploidy detected by day 3 preimplantation genetic screening (PGS) biopsy and the effect on subsequent implantation and pregnancy rates. DESIGN: Retrospective cohort study. SETTING: University-based IVF center. PATIENT(S): A total of 141 couples undergoing their first IVF-PGS cycle for idiopathic recurrent pregnancy loss (RPL) or multiple failed IVF implantations. INTERVENTION(S): Biopsy of single-nucleated blastomeres for PGS analysis of chromosomes X, Y, 13, 15, 16, 17, 18, 21, and 22 by fluorescence in situ hybridization. MAIN OUTCOME MEASURE(S): Aneuploidy, implantation, and pregnancy rates. RESULT(S): PGS revealed an increased incidence of aneuploidy when comparing multinucleated day 2 embryos with single-nucleated embryos (85% vs. 78%; relative risk 0.92 (95% confidence interval 0.84-1.00). Transfer of single-nucleated euploid embryos resulted in clinical pregnancy and implantation rates of 28% and 24%. Transfer of multinucleated euploid embryos resulted in no implantations. CONCLUSION(S): The presence of multinucleated blastomeres on day 2 of embryo development, 1 day before biopsy, predicts an increase of aneuploidy and poor pregnancy outcomes in IVF-PGS cycles.

Simulated yields of prospective specimen collection from specific patient cohorts using retrospective data from a research patient data repository.

Biospecimens obtained during the process of care may be a potential resource for bioinformatics research requiring specimens from specific patient populations. In order to estimate the potential yield of collecting specimens obtained during the process of care, we used a research patient data registry to define specific patient cohorts, and then used these cohorts to screen for chemistry and hematology tests also contained in the registry. We present here our approach and initial results.

Mining for associations between categorical data items in a clinical data repository.

We present here our preliminary work in using simple two-way categorical tests to discover associations between categorical items in a clinical data repository. Initial results using the chi square test yielded diagnosis code associations that seemed plausible as well as several that did not. This may be due in part to the effect of sample size. Tests more resistant to the effects of sample size may yield a higher fraction of plausible diagnosis code associations.

Using rough sets, neural networks, and logistic regression to predict compliance with cholesterol guidelines goals in patients with coronary artery disease.

Coronary artery disease is a leading cause of death and disability in the United States and throughout the developed world. Results from large randomized, blinded, placebo-controlled trials have demonstrated clearly the benefit of lowering LDL cholesterol in lowering the risk for coronary artery disease. Unfortunately, despite the quantity of evidence, and the availability of medications that can efficiently lower LDL cholesterol with few side effects, not everyone who could benefit from cholesterol lowering interventions actually receives them. Despite the dissemination of national care guidelines for the evaluation and treatment of cholesterol levels (NCEP - National Cholesterol Education Program), compliance with such guidelines is suboptimal. There clearly is room for improvement in narrowing the gap between evidence based guidelines and actual clinical practice. The ability to classify those patients who are or will likely to be noncompliant on the basis of patient data routinely collected during patient care could be potentially useful by enabling the focusing of limited health care resources to those who are or will be at high risk of being under treated. In order to explore this possibility further, we attempted to create such classifiers of cholesterol guideline compliance. To do this, we obtained data from an ambulatory electronic medical record system at use at the MGH adult primary care practices for over 20 years. We obtained the data from this hierarchically-structured EMR using its own native query language, called MQL (Medical Query Language). Next, we applied to the collected data the machine learning techniques of rough set theory, neural networks (feed forward backpropagation nets), and logistic regression. We did this by using commonly available software that for the most part is freely available via the internet. We then compared the accuracy of the classifier models using the receiver operating characteristic (ROC) area and C-index summary metrics.