[Hemoptysis and acute kidney injury in COVID-19]

The present case describes the diagnostic and therapy of the pulmorenal syndrome of a 40-year-old patient who presented in our clinic due to increasing respiratory insufficiency and hemoptysis with confirmed COVID-19 disease.

Gold nanorods with conjugated polymer ligands: sintering-free conductive inks for printed electronics

Metal-based nanoparticle inks for printed electronics usually require sintering to improve the poor electron transport at particle-particle interfaces. The ligands required for colloidal stability act as insulating barriers and must be removed in a post-deposition sintering step. This complicates the fabrication process and makes it incompatible with many flexible substrates. Here, we bind a conjugated, electrically conductive polymer on gold nanorods (AuNRs) as a ligand. The polymer, poly[2-(3-thienyl)-ethyloxy-4-butylsulfonate] (PTEBS), provides colloidal stability and good electron transport properties to stable, sintering-free inks. We confirm that the polymer binds strongly through a multidentate binding motif and provides superior colloidal stability in polar solvents over months by IR and Raman spectrometry and zeta potential measurements. We demonstrate that the developed ligand exchange protocol is directly applicable to other polythiophenes such as poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS). Films of AuNRs coated with above polymers reached conductivities directly after deposition comparable to conventional metal inks after ligand removal and retained their conductivity for at least one year when stored under ambient conditions.

Multivalent bonds in self-assembled bundles of ultrathin gold nanowires

We describe solvent effects in the self-assembly of ultrathin gold nanowires and highlight the role of intermolecular ligand–solvent interactions.

Microglial stability and repopulation in the retina

BACKGROUND/AIMS

Parenchymal central nervous system microglia are repopulated by bone marrow derived monocytes more slowly than any other reticuloendothelial cells. The contribution of bone marrow derived monocytes to the uninflammed retina has not been studied. The present study sought to determine repopulation of retinal microglia in uniflammed retina by bone marrow derived monocytes in bone marrow chimeric rats.

METHODS

Chimeric (Y-->X) Lewis rats were constructed by transplanting 5 x 10(7) male bone marrow cells into lethally irradiated female recipient rats. The chimeras were sacrificed 8, 10, 12, 30, and 52 weeks after bone marrow transplant, and retina, brain, lung, and spleen samples were collected. DNA was extracted and quantified. Y positive infiltrating cells in the collected samples were detected by polymerase chain reaction amplification of a Y chromosome specific 104 bp fragment.

RESULTS

There was a rapid repopulation of haematopoietic tissues in the spleen (at 8 weeks), confirming the establishment of chimerism, and to a lesser extent, of lung (at 30 weeks). This repopulation was absent in the brain parenchyma and retina until 52 weeks after transplantation.

CONCLUSIONS

These data indicate that resident microglia in the retina, much like those in the brain, are stable in number in the retinal compartment (up to 1 year), and repopulation by bone marrow derived cells may be delayed for a year.

Confidence intervals for the 50 per cent response dose

Confidence intervals for the 50 per cent response dose are usually computed using either the Delta method or Fieller's procedure. Recently, confidence intervals computed by inverting the asymptotic likelihood ratio test have also been recommended. There is some controversy as to which of these methods should be used. By means of an extensive simulation study we examine these methods as well as confidence intervals obtained by the approximate bootstrap confidence (ABC) procedure and an adjusted form of the likelihood ratio based confidence intervals.

Minimal and best linear combination of oxidative stress and antioxidant biomarkers to discriminate cardiovascular disease

BACKGROUND AND AIM

Free radicals have been implicated in the atherosclerotic process of coronary heart disease (CHD). Well-developed laboratory methods may make available a large number of biomarkers of individual oxidative stress and antioxidant status. Such markers are able to quantify different phases of the oxidative stress and antioxidant status of an individual. However, limited knowledge is available on how to combine these biomarkers to best discriminate between individuals with and without CHD.

METHODS AND RESULTS

We evaluated combined discrimination properties of six biomarkers of oxidative stress and antioxidant status, as indicators of CHD, in a cross-sectional random sample of 968 white men and women from Buffalo, New York. Individuals with CHD had significantly higher levels of thiobarbuturic acid reacting substances (TBARS) and uric acid, and significantly lower levels of high-density lipoproteins (HDL) after adjusting for age and gender, when compared to healthy subjects. There were no significant differences in erythrocyte glutathione (GSH), trolox equivalent antioxidant capacity (TEAC), and glutathione peroxidase (GSHPx) levels. TBARS were found to be the best discriminating of the biomarkers when it was individually evaluated. TBARS discriminate 76.2% (95% C.I. 0.66-0.82) of the CHD cases from healthy controls. When combining TBARS, GSH, TEAC, HDL, uric acid and GSHPx, they discriminate 81.5% (95% C.I.: 0.67-0.90) of the area under the curve.

CONCLUSIONS

These preliminary findings suggest that the combination of multiple markers of oxidative stress does not greatly improve ability to differentiate between individuals with and without CHD compared to the use of TBARS alone.

Criticality of predictors in multiple regression

A new method is proposed for comparing all predictors in a multiple regression model. This method generates a measure of predictor criticality, which is distinct from and has several advantages over traditional indices of predictor importance. Using the bootstrapping (resampling with replacement) procedure, a large number of samples are obtained from a given data set which contains one response variable and p predictors. For each sample, all 2p-1 subset regression models are fitted and the best subset model is selected. Thus, the (multinomial) distribution of the probability that each of the 2p-1 subsets is 'the best' model for the data set is obtained. A predictor's criticality is defined as a function of the probabilities associated with the models that include the predictor. That is, a predictor which is included in a large number of probable models is critical to the identification of the best-fitting regression model and, therefore, to the prediction of the response variable. The procedure can be applied to fixed and random regression models and can use any measure of goodness of fit (e.g., adjusted R2, Cp, AIC) for identifying the best model. Several criticality measures can be defined by using different combinations of the probabilities of the best-fitting models, and asymptotic confidence intervals for each variable's criticality can be derived. The procedure is illustrated with several examples.

mROC: a computer program for combining tumour markers in predicting disease states

Receiver operating characteristic (ROC) curves are limited when several diagnostic tests are available, mainly due to the problems of multiplicity and inter-relationships between the different tests. The program presented in this paper uses the generalised ROC criteria, as well as its confidence interval, obtained from the non-central F distribution, as a possible solution to this problem. This criterion corresponds to the best linear combination of the test for which the area under the ROC curve is maximal. Quantified marker values are assumed to follow a multivariate normal distribution but not necessarily with equal variances for two populations. Other options include Box-Cox variable transformations, QQ-plots, interactive graphics associated with changes in sensitivity and specificity as a function of the cut-off. We provide an example to illustrate the usefulness of data transformation and of how linear combination of markers can significantly improve discriminative power. This finding highlights potential difficulties with methods that reject individual markers based on univariate analyses.

TBARS and cardiovascular disease in a population-based sample

BACKGROUND

Oxygen radicals might play a crucial role in the pathogenesis of various diseases, including atherosclerosis. Thiobarbituric acid reaction substances (TBARS), a biomarker of oxidative stress, have been proposed as a summary measure of total circulating oxidation. However, there is no strong indication that circulating levels of TBARS are increased in patients with atherosclerosis.

DESIGN

We evaluated the relation between TBARS and cardiovascular disease (CVD) in a cross-sectional random sample of white men and women from Buffalo, New York.

METHODS

Logistic regression was used to estimate the risk associated with high levels of TBARS. The area under the ROC curve was used to evaluate the discriminating power of TBARS.

RESULTS

After adjusting for age and gender, TBARS levels were significantly higher in those with prevalent CVD (OR= 1.73, 95% CI=1.32-2.38), compared to those without a CVD diagnosis. These OR were almost 50% higher after correcting for measurement error (ME) (OR=1.93, 95% CI=1.07-3.40). The area under the ROC curve was 0.69 (95% CI=0.62-0.77) and when corrected for ME reached 0.80 (95% CI=0.65-0.89).

CONCLUSIONS

Our results indicate that elevated levels of TBARS were associated with increase risk of the prevalence of CVD, but this effect was no longer significant after adjusting for glucose.

Statistical inference for the area under the receiver operating characteristic curve in the presence of random measurement error

The area under the receiver operating characteristic curve is the most commonly used measure of the ability of a biomarker to distinguish between two populations. Some markers are subject to substantial measurement error. Under normality assumptions, the authors develop a confidence interval procedure for the area under the receiver operating characteristic curve that adjusts for measurement error. This procedure assumes the availability of data from a reliability study of the biomarker. A simulation study was used to check the validity of the proposed confidence interval. Furthermore, it was shown that not adjusting for measurement error could result in a serious understatement of the effectiveness of the biomarker.

Measuring the effectiveness of diagnostic markers in the presence of measurement error through the use of ROC curves

The area under the receiver operating characteristic curve is frequently used to assess the effectiveness of diagnostic markers in distinguishing between diseased and healthy individuals. These markers are generally subject to measurement error. In this paper confidence intervals for the area under the curve are developed which take measurement error into account. These intervals depend on the availability of replicated observations for the subject. Both equal and unequal numbers of replicates per subject are considered. The practice of averaging over replicates and then ignoring measurement error is examined and found wanting.

[Generalized ROC criteria in the evaluation of several tumor markers]

The main objective of this paper is to present a method of evaluating several tumor markers by using the generalized ROC criterion. This criterion finds the best linear combination of the tumor markers such that the area under the ROC curve is maximized. Confidence intervals for the generalized ROC criteria are also presented. This methodology is applied to 51 patients with advanced colorectal cancer for whom the ACE tumor markers were measured before and during chemotherapy treatment. Two populations were defined according to clinical response to chemotherapy. Each marker taken separately, whether on the raw scale or on the transformed scale, contained 0.5 in the confidence interval and was thus non significant. This was also true for both markers on the raw scale. However, the best linear combination on the logarithms of ACE before and at evaluation gave a significantly better area under the ROC curve. A weighted change in ACE measurements significantly distinguishes between responders and non responders in patients with advanced colorectal cancer. We propose that the methodology presented in this paper be used for the evaluation of several tumor markers.

Cognitive knowledge decline after Advanced Trauma Life Support courses

OBJECTIVE

To assess the cognitive knowledge decline among graduates of the Advanced Trauma Life Support (ATLS) program in Israel, to compare the rate of decline between surgeons and nonsurgeons, and to recommend appropriate timing for refresher courses.

METHODS

A prospective study based on multiple-choice question test results of 220 ATLS course graduates was conducted 3 to 60 months after course completion. These results were then compared with the examination results immediately after the course. A statistical model based on survival analysis was used to evaluate the decline pattern and extent and to compare the study groups.

RESULTS

A significant decline of cognitive knowledge over time among ATLS graduates was demonstrated. This decline was significantly greater in the nonsurgical group. A critical point of 20% cognitive knowledge loss among 50% of the examined physicians was observed around the 180th week after completion of the course.

CONCLUSION

Physicians taking the ATLS course lose a significant part of their acquired cognitive knowledge after 3.5 years. Surgeons retain their cognitive knowledge for longer periods of time. Based on the study results, the optimal timing for a refresher course is between 3 and 4 years after the initial ATLS course.

The accumulated experience of the Israeli Advanced Trauma Life Support program

BACKGROUND

Between January 1990 and May 1995 one faculty in Israel taught Advanced Trauma Life Support (ATLS) courses to 3,700 physicians. Two types of courses were given to three subpopulations. We studied the influence of demographic variables on students' achievements in the course and compared students' achievements as a function of their course type.

STUDY DESIGN

This study was conducted as a concurrent longitudinal study.

RESULTS

Achievements of 3,700 students were analyzed. The precourse grade, type of course, and their interaction were found to have a significant effect on the postcourse grades. Physicians practicing surgical subspecialties, in general, did better, as did students educated in English-speaking countries. Students who took part in the Combat Trauma Life Support (CTLS) course, which included the entire ATLS course and additional lectures and exercises, also ended with better scores.

CONCLUSIONS

Physician's country of origin and clinical subspecialty have a significant effect on the cognitive achievement in the ATLS course provided in Israel. An expanded ATLS course (CTLS), to include additional military trauma topics as well as additional skill station training, can improve the results of the postcourse grades.

Confidence intervals for the generalized ROC criterion

Receiver operating characteristic (ROC) curves are frequently used to assess the usefulness of diagnostic markers. When several diagnostic markers are available, they can be combined by a best linear combination: that is, when the area under the ROC curve of this combination is maximized among all possible linear combinations. This maximal area is the generalized ROC criterion, which provides a measure of how effective the combination of the markers is. This criterion needs to be estimated from the data, and is usually evaluated against single markers. In the present paper, we provide confidence intervals for the generalized ROC criterion under the assumption of homogeneous covariance matrices, derive an approximation for the heterogeneous covariance matrices case, and evaluate the approximation via a simulation study. Finally, we present an illustrative example.

Dynamic treatment allocation adjusting for prognostic factors for more than two treatments

Methods of sequential allocation of one of K treatments to patients, while controlling for important prognostic factors, are developed and compared. We focus on methods that are based on optimality theory (Begg and Iglewicz, 1980, Biometrics 36, 81-90; Atkinson, 1982, Biometrika 69, 61-67), the permuted block procedure (Zelen, 1974, Journal of Chronic Diseases 27, 365-375), and the compromise method (Faraggi and Reiser, 1991, Communication in Statistics, Simulation and Computation 20, 243-254). These methods are extended to the K treatments case and are evaluated in terms of efficiency and balance. It is shown that each method achieved the best results in the criterion it was designed to optimize, i.e., within stratum balance for the permuted block allocation and efficiency for the allocations that are based on optimality theory, but did not do well with other criteria. The compromise method, on the other hand, has good overall properties in terms of both balance and efficiency.

Dependent masking and system life data analysis: Bayesian inference for two-component systems

Data from field operations of a system is often used to estimate the reliability of components. Under ideal circumstances, this system field data contains the time to failure along with information on the exact component responsible for the system failure. However, in many cases, the exact component causing the failure of the system cannot be identified, and is considered to be masked. Previously developed models for estimation of component reliability from masked system life data have been based upon the assumption that masking occurs independently of the true cause of system failure. In this paper we develop a Bayesian methodology for estimating component reliabilities from masked system life data when the probability of masking is dependent upon the true cause of system failure. The Bayesian approach is illustrated for the case of a two-component system of exponentially distributed components.

Alternative estimation procedures for Pr(X less than Y) in categorized data

Consider two independent random variables X and Y. The functional R = Pr(X less than Y) [or gamma = Pr(X less than Y) - Pr(Y less than X)] is of practical importance in many situations, including clinical trials, genetics, and reliability. In this paper several approaches to estimation of gamma when X and Y are presented in discretized (categorical) form are analyzed and compared. Asymptotic formulas for the variances of the estimators are derived; use of the bootstrap to estimate variances is also discussed. Computer simulations indicate that the choice of the best estimator depends on the value of gamma, the underlying distribution, and the sparseness of the data. It is shown that the bootstrap provides a robust estimate of variance. Several examples are treated.