A data-driven optimization algorithm for differential algebraic equations with numerical infeasibilities

Support Vector Machines (SVMs) based optimization framework is presented for the data-driven optimization of numerically infeasible Differential Algebraic Equations (DAEs) without the full discretization of the underlying first-principles model. By formulating the stability constraint of the numerical integration of a DAE system as a supervised classification problem, we are able to demonstrate that SVMs can accurately map the boundary of numerical infeasibility. The necessity of this data-driven approach is demonstrated on a 2-dimensional motivating example, where highly accurate SVM models are trained, validated, and tested using the data collected from the numerical integration of DAEs. Furthermore, this methodology is extended and tested for a multi-dimensional case study from reaction engineering (i.e., thermal cracking of natural gas liquids). The data-driven optimization of this complex case study is explored through integrating the SVM models with a constrained global grey-box optimization algorithm, namely the ARGONAUT framework.

Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms

Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as endocrine-disrupting chemicals (EDCs). Here, we focused on a subclass of EDCs that impacts the estrogen receptor (ER), a pivotal transcriptional regulator in health and disease. Estrogenic activity of compounds can be measured by many in vitro or cell-based high throughput assays that record various endpoints from large pools of cells, and increasingly at the single-cell level. To simultaneously capture multiple mechanistic ER endpoints in individual cells that are affected by EDCs, we previously developed a sensitive high throughput/high content imaging assay that is based upon a stable cell line harboring a visible multicopy ER responsive transcription unit and expressing a green fluorescent protein (GFP) fusion of ER. High content analysis generates voluminous multiplex data comprised of minable features that describe numerous mechanistic endpoints. In this study, we present a machine learning pipeline for rapid, accurate, and sensitive assessment of the endocrine-disrupting potential of benchmark chemicals based on data generated from high content analysis. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, both linear logistic regression and nonlinear Random Forest classifiers were benchmarked and evaluated for predicting the estrogenic activity of unknown compounds. Furthermore, through feature selection, data visualization, and model discrimination, the most informative features were identified for the classification of ER agonists/antagonists. The results of this data-driven study showed that highly accurate and generalized classification models with a minimum number of features can be constructed without loss of generality, where these machine learning models serve as a means for rapid mechanistic/phenotypic evaluation of the estrogenic potential of many chemicals.

Chemical contaminants or toxicants pose environmental and health-related risks for exposure. The ability to rapidly understand their biological impact, specifically on a key modulator of important physiological and pathological states in the human body is essential for diagnosing and avoiding undesirable health outcomes during environmental emergencies. In this study, we use advanced data analytics for creating statistical models that can accurately predict the endocrinological activity of toxic chemicals based on high throughput/high content image analysis data. We focus on a subclass of chemicals that affect the estrogen receptor (ER), which is a pivotal transcriptional regulator in health and disease. The multidimensional imaging data of these benchmark chemicals are used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, we evaluate linear and nonlinear classifiers for predicting the estrogenic activity of unknown compounds and use feature selection, data visualization, and model discrimination methodologies to identify the most informative features for the classification of ER agonists/antagonists.

DOMINO: Data-driven Optimization of bi-level Mixed-Integer NOnlinear Problems

The Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems (DOMINO) framework is presented for addressing the optimization of bi-level mixed-integer nonlinear programming problems. In this framework, bi-level optimization problems are approximated as single-level optimization problems by collecting samples of the upper-level objective and solving the lower-level problem to global optimality at those sampling points. This process is done through the integration of the DOMINO framework with a grey-box optimization solver to perform design of experiments on the upper-level objective, and to consecutively approximate and optimize bi-level mixed-integer nonlinear programming problems that are challenging to solve using exact methods. The performance of DOMINO is assessed through solving numerous bi-level benchmark problems, a land allocation problem in Food-Energy-Water Nexus, and through employing different data-driven optimization methodologies, including both local and global methods. Although this data-driven approach cannot provide a theoretical guarantee to global optimality, we present an algorithmic advancement that can guarantee feasibility to large-scale bi-level optimization problems when the lower-level problem is solved to global optimality at convergence.

Integrated Data-Driven Process Monitoring and Explicit Fault-Tolerant Multiparametric Control

We propose a novel active fault-tolerant control strategy that combines machine learning based process monitoring and explicit/multiparametric model predictive control (mp-MPC). The strategy features (i) data-driven fault detection and diagnosis models by using the support vector machine (SVM) algorithm, (ii) ranking via a nonlinear, kernel-dependent, SVM-based feature selection algorithm, (iii) data-driven regression models for fault magnitude estimation via the random forest algorithm, and (iv) a parametric optimization and control (PAROC) framework for the design of the explicit/multiparametric model predictive controller. The resulting explicit control strategies correspond to affine functions of the system states and the magnitude of the detected fault. A semibatch process, an example for penicillin production, is presented to demonstrate how the proposed framework ensures smart operation for which rapid switches between a priori computed explicit control action strategies are enabled by continuous process monitoring information.

Grouping of complex substances using analytical chemistry data: A framework for quantitative evaluation and visualization

A detailed characterization of the chemical composition of complex substances, such as products of petroleum refining and environmental mixtures, is greatly needed in exposure assessment and manufacturing. The inherent complexity and variability in the composition of complex substances obfuscate the choices for their detailed analytical characterization. Yet, in lieu of exact chemical composition of complex substances, evaluation of the degree of similarity is a sensible path toward decision-making in environmental health regulations. Grouping of similar complex substances is a challenge that can be addressed via advanced analytical methods and streamlined data analysis and visualization techniques. Here, we propose a framework with unsupervised and supervised analyses to optimally group complex substances based on their analytical features. We test two data sets of complex oil-derived substances. The first data set is from gas chromatography-mass spectrometry (GC-MS) analysis of 20 Standard Reference Materials representing crude oils and oil refining products. The second data set consists of 15 samples of various gas oils analyzed using three analytical techniques: GC-MS, GC×GC-flame ionization detection (FID), and ion mobility spectrometry-mass spectrometry (IM-MS). We use hierarchical clustering using Pearson correlation as a similarity metric for the unsupervised analysis and build classification models using the Random Forest algorithm for the supervised analysis. We present a quantitative comparative assessment of clustering results via Fowlkes-Mallows index, and classification results via model accuracies in predicting the group of an unknown complex substance. We demonstrate the effect of (i) different grouping methodologies, (ii) data set size, and (iii) dimensionality reduction on the grouping quality, and (iv) different analytical techniques on the characterization of the complex substances. While the complexity and variability in chemical composition are an inherent feature of complex substances, we demonstrate how the choices of the data analysis and visualization methods can impact the communication of their characteristics to delineate sufficient similarity.

P5345Association and prevalence of post-stroke erectile dysfunction with cardiovascular risk factors and co-morbidities

Objectives

The aim of this study was to establish a correlation between prevalence and severity of erectile dysfunction (ED) and cardiovascular (CV) co-morbidities and ongoing medication and other risk factors associated with post-stroke ED.

Materials and methods

For 153 patients (57.04±6.54 years) with ischemic stroke, we evaluated the pre- and post-stroke prevalence of ED using the five-item International Index of Erectile Function questionnaire (IIEF5).

Erectile Function questionnaire (IIEF5). Within 5 days of admission we determined the stroke site location and severity using the National Institute of Health Stroke Scale (NIHSS). The pre- and post-stroke data obtained were compared with those of 30 control non-stroke patients (52.27±8.35). Additional cardiovascular co-morbidities, medication and risk factors were asset and analyzed.

Results

The IIEF5 scores were much lower [median 17 interquartile range (IQR) 10–20] post stroke than pre-stroke (median 22 IQR 12–23) and lower than in control group (median 22.5 IQR 21–24).

From the analysis of comorbidities and risk factors for stroke of post- stroke group and the control group, we infer that diabetes (p=0.003), hypercholesterolemia (p<0.001), and hypertension (p<0.001) were more common in patients with stroke than those in the control group. (Table 1).

From the statistical analysis of data on medication use by patients, results that more patients have used ACE inhibitors, calcium antagonists, beta blocking agents, diuretics, statins, oral agents, antiplatelet and oral anticoagulants after the stoke than before, and in terms of consumption of drugs before stroke compared with the control group, differences were not significant.

Lot 1 Lot 2 Lot 3 P values P values P values post-stoke patients pre-stroke patients control group [1 vs 3] [2 vs 3] [1 vs 2] No. of Patients 153 153 30 Age, mean ± SD 57.04±6.54 57.04±6.54 52.27±8.35 Pacient with ED, N (%) 127 (83%) 76 (49.67%) 9 (30%) <0.001 0.048 <0.001 Severity of ED, N (%)   Mild 74 (48.37%) 29 (18.95%) 7 (23.33%) 0.015 0.581 <0.001   Mild to moderate 1 (0.01%) 11 (7.19%) 1 (3.33%) 0.302* 0.694* 0.127*   Moderate 28 (18.30%) 21 (13.73%) 1 (3.33%) 0.052* 0.134* <0.001*   Severe 24 (15.69%) 15 (9.80%) 0 0.016* 0.136* <0.001* IIEF5 (Erectile function)   Mean ± SD 15.53±5.89 17.83±6.18 21.83±3.31 <0.001 <0.001 <0.001   Median (Q1–Q3) 17 (10–20) 22 (12–23) 22.5 (21–24) Hamilton Score   Normal 91 (59.4%) 144 (94.1%) 23 (76.6%)   Mild depression 40 (26.1%) 1 (0.6%) 5 (16.6%)   Moderate depression 11 (7.1%) 0 (0.0%) 1 (3.3%)   Severe depression 9 (5.8%) 6 (3.9%) 1 (3.3%)   Very severe depression 2 (1.3%) 2 (1.3%) 0 (0.0%) Comorbidities   Diabetes mellitus 59 (38.5%) 3 (10.0%) 0.003*   Hypercholesterolemia 104 (67.9%) 6 (20.0%) <0.001   Hypertension 121 (79.0%) 8 (26.6%) <0.001   Obesity 36 (23.5%) 6 (20.0%) 0.674   Smoking 53 (34.6%) 5 (16.6%) 0.056*   Atrial fibrillation 22 (14.3%) 2 (6.6%) 0.377*   Carotid artery stenosis 18 (11.7%) 1 (3.3%) 0.321*   Coronary hearth disease 26 (16.9%) 1 (3.3%) 0.086* Medication   ACE inhibitors 72 (47.0%) 32 (20.9%) 2 (6.6%) <0.001* 0.075* <0.001   Calcium Antagonists 49 (32.0%) 17 (11.1%) 4 (13.3%) 0.047* 0.755* <0.001   Beta-Blokers 65 (42.4%) 36 (23.5%) 3 (10.0%) 0.001* 0.142* <0.001   Diuretics 43 (28.1%) 14 (9.1%) 3 (10.0%) 0.039* >0.999* <0.001   Statins 99 (64.7%) 25 (16.3%) 4 (13.3%) <0.001* 0.791* <0.001   Oral antidiabetics 39 (25.4%) 25 (16.3%) 1 (10.0%) 0.007* 0.084* 0.442   Insulin 20 (13.0%) 15 (9.8%) 0 (0.0%) 0.048* 0.136* 0.369   Antiplatelet drugs 131 (85.6%) 14 (9.1%) 2 (6.6%) <0.001* >0.999* <0.001   Oral anticoagulants 22 (14.3%) 8 (5.2%) 0 (0.0%) 0.028* 0.357* 0.007   Antidepressants 28 (18.3%) 12 (7.84%) 2 (6.6%) 0.176* >0.999* 0.007

Conclusions

The prevalence and severity of ED increase after stroke due to disruption of autonomous central structures. The depression, functional impairment, CV co-morbidities and medication used after stroke may contribute to ED.

A Nonlinear Support Vector Machine-Based Feature Selection Approach for Fault Detection and Diagnosis: Application to the Tennessee Eastman Process

In this article, we present (1) a feature selection algorithm based on nonlinear support vector machine (SVM) for fault detection and diagnosis in continuous processes and (2) results for the Tennessee Eastman benchmark process. The presented feature selection algorithm is derived from the sensitivity analysis of the dual C-SVM objective function. This enables simultaneous modeling and feature selection paving the way for simultaneous fault detection and diagnosis, where feature ranking guides fault diagnosis. We train fault-specific two-class SVM models to detect faulty operations, while using the feature selection algorithm to improve the accuracy and perform the fault diagnosis. Our results show that the developed SVM models outperform the available ones in the literature both in terms of detection accuracy and latency. Moreover, it is shown that the loss of information is minimized with the use of feature selection techniques compared to feature extraction techniques such as principal component analysis (PCA). This further facilitates a more accurate interpretation of the results.

Simultaneous Fault Detection and Identification in Continuous Processes via nonlinear Support Vector Machine based Feature Selection

Rapid detection and identification of process faults in industrial applications is crucial to sustain a safe and profitable operation. Today, the advances in sensor technologies have facilitated large amounts of chemical process data collection in real time which subsequently broadened the use of data-driven process monitoring techniques via machine learning and multivariate statistical analysis. One of the well-known machine learning techniques is Support Vector Machines (SVM) which allows the use of high dimensional feature sets for learning problems such as classification and regression. In this paper, we present the application of a novel nonlinear (kernel-dependent) SVM-based feature selection algorithm to process monitoring and fault detection of continuous processes. The developed methodology is derived from sensitivity analysis of the dual SVM objective and utilizes existing and novel greedy algorithms to rank features that also guides fault diagnosis. Specifically, we train fault-specific two-class SVM models to detect faulty operations, while using the feature selection algorithm to improve the accuracy of the fault detection models and perform fault diagnosis. We present results for the Tennessee Eastman process as a case study and compare our approach to existing approaches for fault detection, diagnosis and identification.

Optimal Chemical Grouping and Sorbent Material Design by Data Analysis, Modeling and Dimensionality Reduction Techniques

The ultimate goal of the Texas A&M Superfund program is to develop comprehensive tools and models for addressing exposure to chemical mixtures during environmental emergency-related contamination events. With that goal, we aim to design a framework for optimal grouping of chemical mixtures based on their chemical characteristics and bioactivity properties, and facilitate comparative assessment of their human health impacts through read-across. The optimal clustering of the chemical mixtures guides the selection of sorption material in such a way that the adverse health effects of each group are mitigated. Here, we perform (i) hierarchical clustering of complex substances using chemical and biological data, and (ii) predictive modeling of the sorption activity of broad-acting materials via regression techniques. Dimensionality reduction techniques are also incorporated to further improve the results. We adopt several recent examples of chemical substances of Unknown or Variable composition Complex reaction products and Biological materials (UVCB) as benchmark complex substances, where the grouping of them is optimized by maximizing the Fowlkes-Mallows (FM) index. The effect of clustering method and different visualization techniques are shown to influence the communication of the groupings for read-across.

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research.

Big Data Approach to Batch Process Monitoring: Simultaneous Fault Detection and Diagnosis Using Nonlinear Support Vector Machine-based Feature Selection

This paper presents a novel data-driven framework for process monitoring in batch processes, a critical task in industry to attain a safe operability and minimize loss of productivity and profit. We exploit high dimensional process data with nonlinear Support Vector Machine-based feature selection algorithm, where we aim to retrieve the most informative process measurements for accurate and simultaneous fault detection and diagnosis. The proposed framework is applied to an extensive benchmark dataset which includes process data describing 22,200 batches with 15 faults. We train fault and time-specific models on the prealigned batch data trajectories via three distinct time horizon approaches: one-step rolling, two-step rolling, and evolving which varies the amount of data incorporation during modeling. The results show that two-step rolling and evolving time horizon approaches perform superior to the other. Regardless of the approach, proposed framework provides a promising decision support tool for online simultaneous fault detection and diagnosis for batch processes.

Prevalence of Human Herpesvirus-8 and BK Polyoma Virus Infections in End-stage Renal Disease and the Influence of Renal Transplantation

Viral infections lead to significant morbidity and mortality in kidney transplant recipients. We evaluated 49 kidney transplant recipients for human herpesvirus 8 (HHV-8) and BK polyomavirus infections in conjunction with data obtained from 43 donors. The seroprevalence of HHV-8 was 6.9% in donors and 12.2% in recipients. HHV-8 DNA was detected below the limit of quantification (<5000 copies/mL) in a recipient with HHV-8 seropositivity at the pretransplant period and was undetectable at month 3 after transplantation. Transient viruria with BK polyomavirus was recorded in 10.2% of recipients without viremia. Multiple factors contribute to viral reactivation, particularly immunosuppressive treatment. Reduction in maintenance immunosuppression seems beneficial in terms of viral reactivation. At our center, routine use of valganciclovir for antiviral prophylaxis may be effective for the prevention of HHV-8 reactivation.

Steroid-refractory inflammatory bowel disease is a risk factor for CMV infection

OBJECTIVE

Patients with inflammatory bowel disease (IBD) show increased the prevalence of cytomegalovirus (CMV) infection due to the severity of the disease and the immunosuppressive treatments they receive. The aim of this study was to determine the prevalence of CMV infection in IBD patients and identify the risk factors for CMV infection with different demographic characteristics in IBD patients.

PATIENTS AND METHODS

We enrolled 85 patients diagnosed with IBD (43 with ulcerative colitis (UC) and 42 with Crohn's disease (CD)) in this prospective study. The clinical disease activities of UC and CD were assessed using Truelove-Witts and Crohn's disease activity index (CDAI). CMV infection was assessed by detection of DNA using real-time polymerase chain reaction (PCR) in blood samples and quantitative PCR in colonic biopsy specimens and by detection of inclusion bodies using hematoxylin-eosin staining.

RESULTS

Thirteen patients with IBD exhibited concomitant CMV infection. CMV infection was not detected in any of the patients in remission. Viral loads measured in the colonic mucosa of infected patients ranged from 800-7000 genome copies/mL total extracted DNA. The mean serum CMV DNA level was 1694 ± 910 copies/mL (range: 800-3800). The rate of steroid resistance in CMV-positive cases was significantly higher than that in CMV-negative cases (p = 0.001). CD with acute exacerbation was a risk factor for CMV disease (p = 0.04). All of the CMV-positive patients received immunosuppressive treatments.

CONCLUSIONS

CMV infection should be suspected in steroid-resistant UC and CD. Antiviral treatment improved the clinical outcome in steroid-resistant IBD cases with serum CMV DNA levels above 1000 copies/mL.

Highly Accurate Structure-Based Prediction of HIV-1 Coreceptor Usage Suggests Intermolecular Interactions Driving Tropism

HIV-1 entry into host cells is mediated by interactions between the V3-loop of viral glycoprotein gp120 and chemokine receptor CCR5 or CXCR4, collectively known as HIV-1 coreceptors. Accurate genotypic prediction of coreceptor usage is of significant clinical interest and determination of the factors driving tropism has been the focus of extensive study. We have developed a method based on nonlinear support vector machines to elucidate the interacting residue pairs driving coreceptor usage and provide highly accurate coreceptor usage predictions. Our models utilize centroid-centroid interaction energies from computationally derived structures of the V3-loop:coreceptor complexes as primary features, while additional features based on established rules regarding V3-loop sequences are also investigated. We tested our method on 2455 V3-loop sequences of various lengths and subtypes, and produce a median area under the receiver operator curve of 0.977 based on 500 runs of 10-fold cross validation. Our study is the first to elucidate a small set of specific interacting residue pairs between the V3-loop and coreceptors capable of predicting coreceptor usage with high accuracy across major HIV-1 subtypes. The developed method has been implemented as a web tool named CRUSH, CoReceptor USage prediction for HIV-1, which is available at http://ares.tamu.edu/CRUSH/.

Morphological study of bronchial mucosa in the chronic obstructive pulmonary disease under the influence of therapeutic algorithm

OBJECTIVES

Immunohistochemical evaluation of the effectiveness of bronchodilator treatment in patients with chronic obstructive pulmonary disease (COPD).

MATERIALS AND METHODS

There have been examined bronchial mucosa biopsies taken endoscopically from 18 patients with obstructive pulmonary disease. The biopsies were fixed in 4% buffered formalin for 24-48 hours and paraffin inclusion was made using the standard technique. For each biopsy, there were performed 10 serial sections with a thickness of 5 μm. The sections were stained using morphological, histochemical and immunohistochemical methods. At three of the cases, the paraffin blocks were reconverted for the electron microscopy study, in order to assess subcellular details, with special reference to "target" cells involved in local immune response. Morphohistochemical and immunohistochemical analysis was effectuated on biopsies removed before and after the treatment with bronchodilators.

RESULTS

The analysis of the biopsies removed before treatment revealed the following aspects: degenerative alterations of the surface epithelium, loss of ciliary differentiation, absence of caliciform cells, hyperexfoliation, formation of pseudopapillary structures, degenerative lesions of the glands, mucoid and oncocytary metaplasia, stasis in the dilated blood vessels, partly hyalinized wall, multiforme chronic inflammatory infiltrate, myofibroblasts in the depth of lamina propria; argyrophilic basement membrane, fragmentation and lysis of elastic fibers, degranulated mast cells associated with inflammatory infiltrate, with electron-dense typical granules, inflammatory infiltrate with CD20 positive B-lymphocytes, arranged perivasculary and in the vicinity of the basement membrane; rare positive CD4 T-lymphocytes; reduced number of plasma cells. After treatment we found the following aspects: partial or complete regeneration of the covering epithelium, with the presence of cilia cells and occasionally of caliciform cells; remaining myofibroblastic reaction in the lamina propria; increased number of mast cells with minimal or no degranulation; immature, lamelled mast cells.

CONCLUSIONS

The application of management principles in group therapy study was done by the study which aims to demonstrate the beneficial role in COPD therapy of combining a β2-agonist with an anticholinergic, obtaining in this way an additional bronchodilator effect, compared with the one obtained by administrating bronchodilators of type β2 agonists. Deepening the molecular and cellular mechanisms of COPD can lead to more effective methods for early detection of disease, pharmacotherapy targeted and effective conduct exacerbations.

Prevalence of Chlamydia trachomatis and Neisseria gonorrhoeae in Turkey among men With urethritis

BACKGROUND

Chlamydia trachomatis and Neisseria gonorrhoeae are known to cause urethritis. However, only a small number of studies in Eastern European countries have investigated the causes of urethritis.

GOALS

To determine the prevalence of C trachomatis and N gonorrhoeae among men with symptomatic urethritis in Istanbul, Turkey, and to determine whether contact with a commercial sex worker increased the likelihood of chlamydial infections.

STUDY DESIGN

Men with a diagnosis of urethritis at the Istanbul Faculty of Medicine were screened for C trachomatis and N gonorrhoeae by Abbott's ligase chain reaction (LCR) using either urethral swabs or first-void urine. N gonorrhoeae cultures were done on a subset of these patients.

RESULTS

The study enrolled 813 men. All of the men denied condom use during their previous sexual exposures. The overall prevalence of C trachomatis, as determined by LCR, was 15.7%. Only 192 patients were screened for both organisms. N gonorrhoeae prevalence was 9.4%. There was no difference in the chlamydia prevalence between men who had contact with commercial sex workers (CSWs) and men who had no such contact (15.3% versus 17.2%). However, clients of foreign CSWs were more likely to have chlamydia than clients of registered Turkish CSWs.

CONCLUSIONS

C trachomatis and N gonorrhoeae are commonly found in Turkish men with urethritis. The findings did not show more chlamydial infection among men who had contact with CSWs than among men who had no such contact. The failure to use condoms among these men must be addressed.

The treatment of non-gonococcal urethritis with single dose oral azithromycin

In an uncontrolled study, the efficacy of azithromycin in the treatment of non-gonococcal urethritis was assessed in 41 male patients aged between 20 and 40 years with a mean age of 27 +/- 5 years. Clinical and microbiological diagnosis confirmed that 28 men were found positive for Chlamydia trachomatis, 10 for Ureaplasma urealyticum and three for both C. trachomatis and U. urealyticum. All patients received 1 g azithromycin orally (four 250 mg capsules). The length of time between the treatment and following visits were 7-10 days and 14-21 days for second and third visits, respectively. Complete eradication was achieved in 27 out of 41 patients. Of the remaining 14, six were found positive for C. trachomatis and were excluded as they did not return for the follow-up visit, one patient did not achieve complete eradication, one patient infected with both C. trachomatis and U. urealyticum failed to achieve complete eradication, and six patients infected with U. urealyticum failed to be completely cured. No adverse effects were reported in any patient. Single dose administration of 1 g azithromycin appears to be an effective and well-tolerated treatment for chlamydial urethritis and an advantage in terms of patient compliance.