Acute hypersensitivity pneumonitis in a paint quality controller : a case report

Introduction: Isocyanates are sensitizing chemicals used in various industries such as polyurethane foam production or paint-related purposes. Acting as haptens recognized by T-lymphocytes, they can cause allergic asthma and rarely hypersensitivity pneumonitis (HP). We aim to present a case report of acute HP due to hexamethylene diisocyanate (HDI) in a paint quality controller, a profession not generally considered at a high risk for work-related Isocyanates exposure. Case report: A 30-yr-old otherwise healthy female, light smoker working as a paint quality controller developed shortness of breath, malaise, sweating and chills at workplace six hours after handling a HDI-based hardener. Upon admission to emergency department, symptoms had progressed to severe respiratory failure. HR computer tomography (HRCT) showed bilateral ground-glass attenuation without pleural effusion. Rapid clinical and radiological improvement occurred under facial oxygen supply and systemic steroid therapy. Occupational medicine investigations revealed regular handling of HDI using latex gloves without respiratory protection. Assessment at workplace showed insufficient air renewal (1.5 times per hour), inadequate local aspiration and HDI exposure at levels of 1-4.25 ppb/m3 (Swiss Occupation Exposure Limit 5 ppb/m3). Biological monitoring after identical work procedure executed by a co-worker showed HDI exposure (5.1 micrograms hexamethylene diamine/g creatinine). Resumption of work was disadvised because of the life-threatening event. Discussion: The diagnosis of occupational HP is highly supported by classical findings on imagery and typical symptoms occurring within approved latency interval, associated with rapid clinical improvement. Although neither broncho-alveolar lavage nor specific IgG diagnosis (en route) were performed during the acute episode, various blood tests managed to rule out evidence of an infection or autoimmune disease. Other causes of HP seem unlikely as the patient did not have any recurrence of symptoms since absence from work. Workplace evaluation provided significant information on HDI exposure and allowed substantial recommendations to diminish Isocyanate exposure for the 20 still healthy laboratory co-workers. Although the entryways (air or skin) and precise mechanism of toxicity remain unclear, the present case clearly shows that Isocyanates may trigger acute HP in susceptible workers in a profession not generally considered at a high risk.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Low-replicating viruses and strong anti-viral immune response associated with prolonged disease control in a superinfected HIV-1 LTNP elite controller.

OBJECTIVE: To study the causes for the lack of clinical progression in a superinfected HIV-1 LTNP elite controller patient.¦METHODOLOGY AND PRINCIPAL FINDINGS: We studied host genetic, virological and immunological factors associated with viral control in a SI long term non progressor elite controller (LTNP-EC). The individual contained both viruses and maintained undetectable viral loads for >20 years and he did not express any of the described host genetic polymorphisms associated with viral control. None of four full-length gp160 recombinants derived from the LTNP-EC replicated in heterologous peripheral blood mononuclear cells. CTL responses after SI were maintained in two samples separated by 9 years and they were higher in breadth and magnitude than responses seen in most of 250 treatment naïve patients and also 25 controller subjects. The LTNP-EC showed a neutralization response, against 4 of the 6 viruses analyzed, superior to other ECs.¦CONCLUSIONS: The study demonstrated that a strong and sustained cellular and humoral immune response and low replicating viruses are associated with viral control in the superinfected LTNP-EC.

Identification of different heart tissues from MRI C-SENC images using an unsupervised multi-stage fuzzy clustering technique.

PURPOSE: To objectively characterize different heart tissues from functional and viability images provided by composite-strain-encoding (C-SENC) MRI. MATERIALS AND METHODS: C-SENC is a new MRI technique for simultaneously acquiring cardiac functional and viability images. In this work, an unsupervised multi-stage fuzzy clustering method is proposed to identify different heart tissues in the C-SENC images. The method is based on sequential application of the fuzzy c-means (FCM) and iterative self-organizing data (ISODATA) clustering algorithms. The proposed method is tested on simulated heart images and on images from nine patients with and without myocardial infarction (MI). The resulting clustered images are compared with MRI delayed-enhancement (DE) viability images for determining MI. Also, Bland-Altman analysis is conducted between the two methods. RESULTS: Normal myocardium, infarcted myocardium, and blood are correctly identified using the proposed method. The clustered images correctly identified 90 +/- 4% of the pixels defined as infarct in the DE images. In addition, 89 +/- 5% of the pixels defined as infarct in the clustered images were also defined as infarct in DE images. The Bland-Altman results show no bias between the two methods in identifying MI. CONCLUSION: The proposed technique allows for objectively identifying divergent heart tissues, which would be potentially important for clinical decision-making in patients with MI.

Prolonged seroconversion in an elite controller of HIV-1 infection.

HIV-1 diagnosis is usually based on the detection of specific antibodies, appearing in a time-determined pattern following the infection. We describe a prolonged HIV-1 seroconversion in an elite controller (defined as having HIV-1 RNA persistently <50copies/ml while untreated). HIV-1 diagnosis was delayed and complicated by this atypical evolution.

Acute life-threatening extrinsic allergic alveolitis in a paint controller.

BACKGROUND: Occupational diisocyanate-induced extrinsic allergic alveolitis (EAA) is a rare and probably underestimated diagnosis. Two acute occupational EAA cases have been described in this context, but neither of them concerned hexamethylene diisocyanate (HDI) exposure. AIMS: To investigate the cause of a life-threatening EAA arising at work in a healthy 30-year-old female paint quality controller. METHODS: Occupational medical assessment, workplace evaluation, airborne and biological monitoring and immunodermatological tests. RESULTS: Diagnosis of EAA relied on congruent clinical and radiological information, confirmed occupational HDI exposure and positive IgG antibodies and patch tests. The patient worked in a small laboratory for 7 years, only occasionally using HDI-containing hardeners. While working with HDI for 6 h, she developed breathlessness, rapidly progressing to severe respiratory failure. Workplace HDI airborne exposure values ranged from undetectable levels to 4.25 p.p.b. Biological monitoring of urinary hexamethylene diamine in co-workers ranged from <1.0 to 15.4 μg/g creatinine. Patch tests 8 months later showed delayed skin reaction to HDI at 48 h. Subsequent skin biopsy showed spongiotic dermatitis with infiltration of CD4(+) and CD8(+) T cells. CONCLUSIONS: We believe this is the first reported case of acute life-threatening EAA following exposure to HDI. Low concentrations of airborne HDI and relatively high urinary hexamethylene diamine suggest significant skin absorption of HDI could have significantly contributed to the development of this acute occupational EAA.

Fuzzy logic algorithm to extract specific interaction forces from atomic force microscopy data

The atomic force microscope is not only a very convenient tool for studying the topography of different samples, but it can also be used to measure specific binding forces between molecules. For this purpose, one type of molecule is attached to the tip and the other one to the substrate. Approaching the tip to the substrate allows the molecules to bind together. Retracting the tip breaks the newly formed bond. The rupture of a specific bond appears in the force-distance curves as a spike from which the binding force can be deduced. In this article we present an algorithm to automatically process force-distance curves in order to obtain bond strength histograms. The algorithm is based on a fuzzy logic approach that permits an evaluation of "quality" for every event and makes the detection procedure much faster compared to a manual selection. In this article, the software has been applied to measure the binding strength between tubuline and microtubuline associated proteins.

Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape.

Molecular docking is a computational approach for predicting the most probable position of ligands in the binding sites of macromolecules and constitutes the cornerstone of structure-based computer-aided drug design. Here, we present a new algorithm called Attracting Cavities that allows molecular docking to be performed by simple energy minimizations only. The approach consists in transiently replacing the rough potential energy hypersurface of the protein by a smooth attracting potential driving the ligands into protein cavities. The actual protein energy landscape is reintroduced in a second step to refine the ligand position. The scoring function of Attracting Cavities is based on the CHARMM force field and the FACTS solvation model. The approach was tested on the 85 experimental ligand-protein structures included in the Astex diverse set and achieved a success rate of 80% in reproducing the experimental binding mode starting from a completely randomized ligand conformer. The algorithm thus compares favorably with current state-of-the-art docking programs. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Change points detection in crime-related time series: an on-line fuzzy approach based on a shape space representation

The extension of traditional data mining methods to time series has been effectively applied to a wide range of domains such as finance, econometrics, biology, security, and medicine. Many existing mining methods deal with the task of change points detection, but very few provide a flexible approach. Querying specific change points with linguistic variables is particularly useful in crime analysis, where intuitive, understandable, and appropriate detection of changes can significantly improve the allocation of resources for timely and concise operations. In this paper, we propose an on-line method for detecting and querying change points in crime-related time series with the use of a meaningful representation and a fuzzy inference system. Change points detection is based on a shape space representation, and linguistic terms describing geometric properties of the change points are used to express queries, offering the advantage of intuitiveness and flexibility. An empirical evaluation is first conducted on a crime data set to confirm the validity of the proposed method and then on a financial data set to test its general applicability. A comparison to a similar change-point detection algorithm and a sensitivity analysis are also conducted. Results show that the method is able to accurately detect change points at very low computational costs. More broadly, the detection of specific change points within time series of virtually any domain is made more intuitive and more understandable, even for experts not related to data mining.