High-tech pharmacy and home care: a sophisticated partnership.
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Biomedical subjects
Publications and source records attributed to M Feinberg.
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Caffeine, which has been linked to benign breast disease, has an antineoplastic effect in experimental animals, whereas in tissue cultures it inhibits mitoses and induces cell differentiation. We examined caffeine and coffee intake in 101 women with breast cancer to determine whether either or both influence cell differentiation in tumors as well. Nutrient analysis was performed by the Nutrition Coding Center of the University of Minnesota with the Nutrition Data system from the National Heart, Lung, and Blood Institute. Stepwise logistic regression, with tumor differentiation (well and moderate versus poor) as the dependent variable, was used. The analysis indicates that caffeine and/or coffee intake has a significant association with tumor differentiation as women with moderately to well-differentiated tumors had higher caffeine and coffee intake. This raises the question whether caffeine or coffee consumption may help induce cell differentiation and slow tumor growth.
Quality control serum samples and postdexamethasone plasma pools were used to compare 16 commercial cortisol radioimmunoassay kits with the competitive protein-binding assay for plasma glucocorticoids that we used to standardize the dexamethasone suppression test (DST). Thirteen radioimmunoassays gave higher criterion values for the DST than those established using the competitive protein-binding assay. The range of radioimmunoassay criterion values was 4.34 to 8.70 mu mg/dL. Possible explanations are given for these findings, and their importance to the clinical utility of the DST are emphasized. Each laboratory should validate its own criterion cortisol value for depression based on local data, including appropriate control groups.
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The dexamethasone suppression test (DST) can differentiate between endogenous and nonendogenous depression. Similarly, EEG sleep patterns can differentiate primary from secondary depression, and this technique has also been used to make the endogenous-nonendogenous discrimination. However, a number of physiological variables associated with this diagnostic distinction may also affect the DST results and sleep architecture. With the use of multivariate statistical procedures, we found that although age and weight loss affect the results of both tests, both the DST and sleep EEG differentiate endogenous from nonendogenous depression when these variables are taken into account. Severity of illness affected both proposed diagnostic markers, but did not account for the differences between diagnostic groups, alone or when added to the physiological variables. The DST was more sensitive in unipolar than in bipolar endogenous depression, but there were no significant differences in the sleep of unipolar and bipolar patients.
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Higher electromyographic (EMG) activity levels of corrugator and zygomatic face muscles have been reported to be pretreatment predictors of better clinical outcome in depressed patients. We tested this possibility in 29 drug-free, rigorously diagnosed subjects by measuring low-level EMG activity of corrugator and zygomatic muscles during resting and three imagery states (happy, typical day, sad). All patients had major depressive disorder, endogenous subtype. Good responders had significantly higher pretreatment EMG zygomatic values and different EMG profiles. Our findings replicate and expand prior reports.
Several authors have shown that endogenous depressed patients have biological abnormalities which exist to a lesser extent in nonendogenous depressives and in normals, and have suggested that these biological abnormalities may be used as markers to classify depressed patients as endogenous or nonendogenous. We have investigated several of these biological abnormalities, and have explored the diagnostic classifications based on individual markers and on two or more markers in combination. The evaluation of combinations of diagnostic tests requires different statistical techniques than does the evaluation of single tests, and we discuss two such techniques: stepwise multiple regression and contingency table analysis. We report here our results with the dexamethasone suppression test (DST), EEG studies of sleep, and the amphetamine-stimulated release of growth hormone separately and in combination. The amphetamine-stimulated release of growth hormone was not diagnostically useful.
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We derived a discriminant function separating patients with bipolar endogenous depression ("melancholia") from patients with nonendogenous ("neurotic") depression, and showed that the difference between the groups was not one of overall severity of illness alone. The discriminant function (DF) included 5 clinical items. We reduced the DF to a discriminant index (DI) with integral item weights, and trichotomized the DI scores into two definite classifications and an intermediate, uncertain classification. We cross-validated this DI in a separate group of patients, and found no decrease in the accuracy of classification on cross-validation. Thirty-three of 41 (80%) of the patients in the cross-validation group were classified by the DI; 26 of 33 (79%) correctly. We also validated the DI classification against an external, biological marker, the dexamethasone suppression test (DST). The DI predicted the DST result with the same accuracy as the clinical diagnoses did, supporting the validity of the DI.
Laboratory test results for the diagnosis of psychiatric illness usually are reported descriptively despite the ready availability of appropriate inferential statistics. A test's sensitivity, specificity, and diagnostic confidence are conditional probabilities. Confidence intervals may be calculated for these probabilities in any given study. Statistical tests for comparing the results of several studies use techniques for planned and posterior comparisons applied to contingency tables. These established statistical methods aid in the interpretation of laboratory test findings.
Using Candida tenuis, a yeast isolated from the digestive tube of the larva of Phoracantha semipunctata (Cerambycidae, Coleoptera), we were able to demonstrate the bioconversion of citronellal to citronellol. Response surface methodology was used to achieve the optimization of the experimental conditions for that bioconversion process. To study the proposed second-order polynomial model, we used a central composite experimental design with multiple linear regression to estimate the model coefficients of the five selected factors believed to influence the bioconversion process. Only four were demonstrated to be predominant: the incubation pH, temperature, time, and the amount of substrate. The best reduction yields (close to 90%) were obtained with alkaline pH conditions (pH 7.5), a low temperature (25 degrees C), a small amount of substrate (15 mul), and short incubation time (16 h). This methodology was very efficient: only 36 experiments were necessary to assess these conditions, and model adequacy was very satisfactory as the coefficient of determination was 0.9411.
The authors describe three patients who developed withdrawal symptoms after discontinuation of antidepressants. Their symptoms were successfully treated with atropine. Central cholinergic overdrive is implicated in the genesis of the symptoms.
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Biological markers of affective disorders have not been studied intensively in patients with secondary depression. An elderly woman with severe delusional depression secondary to thyrotoxicosis was monitored with weekly dexamethasone suppression tests (DSTs) and three sleep electroencephalography (EEG) evaluations. She received treatment only for her thyrotoxicosis, but her depression resolved completely. The serial DSTs were normal throughout her depression, consistent with the specificity of this test for primary endogenous depression. The sleep EEG erroneously suggested a diagnosis of primary depression but effectively monitored clinical improvement. Biological markers may have applicability in evaluating and monitoring patients with secondary depression.