Serological proteome analysis to identify systemic candidiasis patients in the intensive care unit: Analytical, diagnostic and prognostic validation of anti-Candida enolase antibodies on quantitative clinical platforms

Systemic candidiasis (SC) is associated with high morbidity and mortality, because it generally affects patients with severe underlying diseases and its diagnosis is difficult and often delayed, resulting in delayed therapy. We used serological proteome analysis to screen serum anti‐Candida IgG antibody‐reactivity profiles in 24 patients under intensive care, 12 of which had confirmed SC (fungal cultures), and in 12 healthy subjects. A total of 15 immunogenic proteins from Candida albicans protoplast lysates were differentially immunorecognized by serum IgG antibodies from SC patients compared to controls. Two‐way hierarchical clustering and principal‐component analyses of these antibody‐reactivity patterns accurately differentiated SC patients from controls. Anti‐Eno1p IgG antibodies were found to be present at high abundance in SC patients and be an important molecular fingerprint in serum for SC diagnosis. Differential anti‐Eno1p IgG antibody reactivity was further validated by a tag capture ELISA and a Western blot assay in 45 SC patients and 118 non‐SC subjects. Both quantitative assays provided comparable analytical, diagnostic and prognostic performances, and verified initial proteomic‐profiling results. If confirmed in prospective cohort studies, these anti‐Eno1p IgG antibodies might be useful for SC diagnosis. However, these, at least as measured by these clinical platforms, appear to have limited prognostic value in SC patients.     

Differential expression of serum/plasma proteins in various infectious diseases: Specific or nonspecific signatures

Apart from direct detection of the infecting organisms or biomarker of the pathogen itself, surrogate host markers are also useful for sensitive and early diagnosis of pathogenic infections. Early detection of pathogenic infections, discrimination among closely related diseases with overlapping clinical manifestations, and monitoring of disease progression can be achieved by analyzing blood biomarkers. Therefore, over the last decade large numbers of proteomics studies have been conducted to identify differentially expressed human serum/plasma proteins in different infectious diseases with the intent of discovering novel potential diagnostic/prognostic biomarkers. However, in-depth review of the literature indicates that many reported biomarkers are altered in the same way in multiple infectious diseases, regardless of the type of infection. This might be a consequence of generic acute phase reactions, while the uniquely modulated candidates in different pathogenic infections could be indicators of some specific responses. In this review article, we will provide a comprehensive analysis of differentially expressed serum/plasma proteins in various infectious diseases and categorize the protein markers associated with generic or specific responses. The challenges associated with the discovery, validation, and translational phases of serum/plasma biomarker establishment are also discussed.     

Functional protease profiling for diagnosis of malignant disease

Clinical proteomic profiling by mass spectrometry (MS) aims at uncovering specific alterations within mass profiles of clinical specimens that are of diagnostic value for the detection and classification of various diseases including cancer. However, despite substantial progress in the field, the clinical proteomic profiling approaches have not matured into routine diagnostic applications so far. Their limitations are mainly related to high-abundance proteins and their complex processing by a multitude of endogenous proteases thus making rigorous standardization difficult. MS is biased towards the detection of low-molecular-weight peptides. Specifically, in serum specimens, the particular fragments of proteolytically degraded proteins are amenable to MS analysis. Proteases are known to be involved in tumour progression and tumour-specific proteases are released into the blood stream presumably as a result of invasive progression and metastasis. Thus, the determination of protease activity in clinical specimens from patients with malignant disease can offer diagnostic and also therapeutic options. The identification of specific substrates for tumour proteases in complex biological samples is challenging, but proteomic screens for proteases/substrate interactions are currently experiencing impressive progress. Such proteomic screens include peptide-based libraries, differential isotope labelling in combination with MS, quantitative degradomic analysis of proteolytically generated neo-N-termini, monitoring the degradation of exogenous reporter peptides with MS, and activity-based protein profiling. In the present article, we summarize and discuss the current status of proteomic techniques to identify tumour-specific protease-substrate interactions for functional protease profiling. Thereby, we focus on the potential diagnostic use of the respective approaches.     

Reliability of antibodies to Candida methionine synthase for diagnosis, prognosis and risk stratification in systemic candidiasis: A generic strategy for the prototype development phase of proteomic markers

To be able to diagnose systemic candidiasis (SC) and to predict outcomes in SC patients are still challenging tasks for physicians. Previous proteomic studies suggest that anti-Candida methionine synthase (Met6p) IgG antibodies may be a candidate marker for SC. To evaluate their reliability for diagnosis, prognosis and risk stratification in SC, we developed a generic prototype strategy for their measurement in SC. Receiver-operating-characteristic curve analyses revealed a high diagnostic accuracy for this prototype format, which was slightly better to that for the widely used Western blot assays. Multivariate logistic-regression models showed a positive association between serum anti-Met6p IgG antibody levels and SC risk that was independent from established SC risk factors and other baseline variables. After adjusting for and stratifying according to known prognostic factors, a significant trend toward a lower two-month mortality risk with increasing levels was evidenced in SC patients at presentation. We conclude that these antibodies may be useful in discriminating SC from non-SC patients and determining risk stratification in SC. These may also confer protection against SC and be valuable for the design of future immunotherapies. Furthermore, our prototype format has the potential to make impact on other infectious diseases, cancers, allergies or autoimmune disorders.     

Cross-domain probabilistic inference in a clinical decision support system: examples for dermatology and rheumatology

Introduction

Maintaining a large diagnostic knowledge base (KB) is a demanding task for any person or organization. Future clinical decision support system (CDSS) may rely on multiple, smaller and more focused KBs developed and maintained at different locations that work together seamlessly. A cross-domain inference tool has great clinical import and utility.

Methods

We developed a modified multi-membership Bayes formulation to facilitate the cross-domain probabilistic inferencing among KBs with overlapping diseases. Two KBs developed for evaluation were non-infectious generalized blistering diseases (GBD) and autoimmune diseases (AID). After the KBs were finalized, they were evaluated separately for validity.

Result

Ten cases from medical journal case reports were used to evaluate this “cross-domain” inference across the two KBs. The resultant non-error rate (NER) was 90%, and the average of probabilities assigned to the correct diagnosis (AVP) was 64.8% for cross-domain consultations.

Conclusion

A novel formulation is now available to deal with problems occurring in a clinical diagnostic decision support system with multi-domain KBs. The utilization of this formulation will help in the development of more integrated KBs with greater focused knowledge domains.     

SEREX, Proteomex, AMIDA, and beyond: Serological screening technologies for target identification

Despite the great body of knowledge about the aetiology, pathogenesis, risk factors, and associated molecular processes, cancer remains a prime health concern. Over the past decades scientific and medical research focused on the identification of biomarkers and target molecules for the diagnosis and therapy of cancer. Such markers may allow for improved and early diagnosis, as well as for immunotherapeutic approaches for cancer treatment. A plethora of technologies dedicated to the identification of target molecules was developed including those relying on a humoral response against tumour-associated antigens (TAA) in diseased individuals. As for other diseases, cancers elicit immune responses that result in the induction of T and B lymphocytes specific for tumour-associated proteins, largely self-antigens, but also those comprising viral and bacterial proteins. Cancer-specific serum antibodies are of great use for the isolation and subsequent identification of their cognate antigens. The present review will concentrate on three major serological target identification methods, i.e. SEREX, Proteomex, and AMIDA, concluding with a summary of the milestones in the clinical advancement and applications of serological TAA.     

Proteomics in ocular fluids

The focus of this article is to review recent techniques in proteomic analysis of ocular fluids. These fluids include tears, aqueous humor, and vitreous, they will also be compared to serum analysis. Furthermore, we attempt to summarize some disease correlated biomarkers in ocular fluids that were discovered through different proteomic techniques in eye diseases like dry eye, glaucoma, age-related macular degeneration, uveitis, or diabetic retinopathy. This review is trying to point out the importance of these biomarkers for clinical applications.     

Shotgun immunoproteomics to identify disease‐associated bacterial antigens: Application to human colon cancer

Circulating antibodies reflect a mirror view of invading antigens that are related to infection and cancer. This was recently exemplified by using serum antibodies to capture Streptococcus bovis antigens followed by MS to generate antigen profiles that were diagnostic for colon cancer. These bacterial antigen profiles have a high potential to aid in the immuno‐diagnosis of this disease, as the magnitude of the immune response to bacterial antigens is, in general, superior to the immune response against tumor (self) antigens. In this study, the identity of individual colon cancer‐associated streptococcal antigens was revealed by enrichment of these “diagnostic” antigens by selected patient antibodies followed by high‐accuracy nanoLC‐MS/MS peptide identification. This showed that both the histone‐like protein HlpA and the ribosomal protein Rp L7/L12 are members of the colon cancer‐associated S. bovis immunome. Both antigens also seem to belong to the group of anchorless surface proteins, like 14 additional proteins that were co‐identified in S. bovis cell wall extracts. Among these were the known streptococcal anchorless surface proteins GAPDH and Enolase. Taken together, these data show that shotgun immunoproteomics, combining immunocapture in‐line with LC MS/MS, is a convenient approach for the rapid identification of disease‐associated bacterial antigens.     

The methodology of Dynamic Uncertain Causality Graph for intelligent diagnosis of vertigo

Vertigo is a common complaint with many potential causes involving otology, neurology and general medicine, and it is fairly difficult to distinguish the vertiginous disorders from each other accurately even for experienced physicians. Based on comprehensive investigations to relevant characteristics of vertigo, we propose a diagnostic modeling and reasoning methodology using Dynamic Uncertain Causality Graph. The symptoms, signs, findings of examinations, medical histories, etiology and pathogenesis, and so on, are incorporated in the diagnostic model. A modularized modeling scheme is presented to reduce the difficulty in model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. We resort to the “chaining” inference algorithm and weighted logic operation mechanism, which guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal insights into underlying interactions among diseases and symptoms intuitively demonstrate the reasoning process in a graphical manner. These solutions make the conclusions and advices more explicable and convincing, further increasing the objectivity of clinical decision-making. Verification experiments and empirical evaluations are performed with clinical vertigo cases. The results reveal that, even with incomplete observations, this methodology achieves encouraging diagnostic accuracy and effectiveness. This study provides a promising assistance tool for physicians in diagnosis of vertigo.     

New amperometric immunosensor with response enhanced by PAMAM-dendrimers linked via self assembled monolayers for determination of alpha-fetoprotein in human serum

A new amperometric immunosensor for alpha-fetoprotein (AFP), based on nanobiocomposite substrate and with response enhanced by polyamidoaminic (PAMAM) dendrimers was developed and characterized. The nanostructurated substrate obtained by electrochemical deposition of 100 nm-sized gold nanoparticles on glassy carbon electrodes (GCE) was functionalized by deposition of a SAM of 2-aminoethanethiol (AET), used as linker for the subsequent immobilization of polyamidoaminic dendrimers (PAMAM G.1.5). Two different modes were investigated for the reading of the assay: cyclic voltammetry (CV) or Double Step ChronoAmperometry (DSCA). Satisfying results in terms of response range and precision were reached with both methods. Immunosensors were tested and validated for AFP determination in human serum, showing a limit of detection of 3 ng/mL and a limit of quantitation of 15 ng/mL. The enhanced immunosensor has proved an attractive diagnostic tool able to match the needs of clinical monitoring purposes for AFP quantification in human serum at levels useful both for prognosis of pregnancy progression, and for the identification of the occurrence of neoplastic diseases.     

Distinguishing drug toxicity syndromes from medical diseases: a QMR computer-based approach.

Drug effects can mimic a wide variety of diseases. Experts note that adverse drug reactions (ADRs) have become the 'greatest imitator' of disease in clinical medicine. Quick Medical Reference (QMR) is a decision support system providing diagnostic data about more than 600 medical diseases. Currently, QMR contains only limited drug information. Just as physicians have difficulty diagnosing ADRs, QMR has similar problems in differentiating natural disease manifestations from drug toxicity syndromes. To remedy this problem, two prototype Drug Syndromes (DS), Carbamazepine Toxicity and Penicillin Toxicity, were incorporated into the QMR Knowledge Base (KB). Using detailed case reports, we demonstrated that a DS-augmented version of QMR was successful in discriminating these DS from the other diseases in QMR's KB. The addition of DS significantly improves QMR's diagnostic performance in cases in which some of the pathologic features are the consequence of drugs.     

Surveyance of disease frequency in a population by linkage to diagnostic laboratory databases. A system for monitoring the incidences of hyper- and hypothyroidism as part of the Danish iodine supplementation program

In Denmark an increase in iodine intake through salt iodization has been introduced in 1998. In parallel a program for surveyance of thyroid diseases in the population was developed as recommended by UNICEF and WHO. OBJECTIVE: To develop and evaluate a computer based system to identify and register new cases of hyper- and hypothyroidism in a well defined cohort, by linkage to diagnostic laboratory databases. DESIGN AND RESULTS: (1) Two sub cohorts for monitoring were defined (n=535,859), and evaluated to minimize loss of new cases. Collaboration was established with laboratories covering thyroid hormone analyses in the cohort; (2) a diagnostic algorithm was defined and evaluated against clinical practice; (3) evaluation of the laboratory methods employed by the four participating laboratories, to ensure they would reach the same diagnosis in a patient; (4) a register database was developed which used data imported from the laboratory databases to automatically identify previously unknown cases of hyper- and hypothyroidism and record diagnostic activity in the area. All parts of the registration were carefully evaluated. CONCLUSION: We describe for the first time a computer based system for prospective measuring the incidence rate of hyper- and hypothyroidism. The system is particularly useful for monitoring of iodine supplementation programmes.     

RCD: a toolkit for rheumatic valvular and congenital heart defect diagnosis

Rheumatic valvular heart disease (RVHD) and congenital heart defect (CHD) are one of the major causes of child deaths in Pakistan. According to a survey, prevalence of RVHD in Pakistan is among the highest in the world and 3.9 % of children suffer from CHD annually. If the diagnostic treatment is done earlier, then the diseases can be controlled before reaching the critical stage. Due to the lack of physicians in poor countries, there is a need for an automatic diagnostic toolkit for congenital and RVHD so that the early and accurate diagnosis of disease could be made possible even in the absence of expert physicians. In this paper, a diagnostic toolkit for rheumatic valvular and congenital heart defect is designed and developed using feed-forward back-propagation network based on symptomatic and clinical parameters. The system is built using MatLab toolkit for neural networks and graphical user interface.     

Characterization of a novel antibody immobilization combining protein G with parylene-H for surface plasmon resonance immunosensors

Here we present a method for selectively and efficiently immobilizing antibodies to enhance the detection performance of surface plasmon resonance immune-sensors (SPRIs) for diagnostic applications. To improve the performance of antibody arrays, protein G was used as antibody-selective linkage layer with aldehyde functionalized poly-(para-xylylene) film. To estimate the efficiency of antibody immobilization, immunoglobulin G (IgG) was measured using the anti-IgG immobilized SPRIs. To demonstrate the proof-of-concept validation, the signal detected from the IgG using parylene-H film was compared with that of a combination of parylene-H and protein G in SPRIs. The results showed that the detection of IgG on the immobilized anti-IgG layer using the combination of parylene-H and protein G has a larger change of signal than that of using parylene-H layer. These results also imply that the anti-IgG was densely and efficiently immobilized on the modified surface with the linkage layer in a combination with parylene-H and protein G. Therefore, we believe that this combinatorial approach could selectively immobilize the antibodies, and also be applied for detection and diagnosis of immune diseases in the field of many SPRIs applications.     

Clinical trials among worker populations: the value and significance of anamnestic findings and clinical and instrumental tests for diagnosing work-related musculoskeletal disorders of the upper limbs (WMSDs)

The authors discuss the value and significance of symptoms in WMSDs, in view of the fact that the anamnestic threshold proposed in epidemiological investigations cannot be used as clinical and diagnosing criteria. Some useful clinical procedures are suggested for cases where there is a suspicion of musculoskeletal disorders of the cervical spine and upper limbs, bearing in mind that they are to be applied within the framework of health surveillance programmes undertaken by health care practitioners who are not specialists in orthopaedics, physiatrics or neurology. The recommendations for instrumental tests and specialist referrals are also discussed for the various disorders. The authors also provide flow charts for the diagnostic procedures pertaining to WMSDs. The appendix shows a sample patient chart illustrating the proposed procedures; it also permits the findings to be encoded so that they can be stored in a dedicated database. The codes for diagnosing WMSDs are also reported for the same epidemiological purposes.     

Selecting critical clinical features for heart diseases diagnosis with a real-coded genetic algorithm

In clinic, normally a lot of diagnostic features are recorded from a patient for a certain disease. It will be beneficial for the prompt and correct diagnosis of the disease by selecting the important and relevant features and discarding those irrelevant and redundant ones. In this paper, a real-coded genetic algorithm (GA)-based system is proposed to select the critical clinical features essential to the heart diseases diagnosis. The heart disease database used in this study includes 352 cases, and 40 diagnostic features were recorded for each case. Using the proposed genetic algorithm, 24 critical features have been identified, and their corresponding diagnosis weights for each heart disease of interest have been determined. The critical diagnostic features and their clinic meanings are in sound agreement with those used by the physicians in making their clinic decisions.     

Biomarker discovery in asthma and COPD by proteomic approaches

Asthma and chronic obstructive pulmonary disease (COPD) are multifactorial respiratory diseases, characterized by reversible and irreversible airway obstruction, respectively. Even if the primary causes of these diseases remain unknown, inflammation is a central feature that leads to progressive and permanent pulmonary tissue damage (airway remodeling) up to the total loss of lung function. Therefore, the elucidation of the inflammation mechanisms and the characterization of the biological pathways, involved in asthma and COPD pathogenesis, are relevant in finding new possible diagnostic/prognostic biomarkers and for the validation of new drug targets. In this context, current advances in proteomic approaches, especially those based on MS, provide new tools to facilitate the discovery-driven studies of new biomarkers in respiratory diseases and improve the clinical reliability of the next generation of biomarkers for these diseases consisting of multiple phenotypes. This review will report an overview of the current proteomic methods applied to the discovery of candidate biomarkers for asthma and COPD, giving a special emphasis to emerging MS-based techniques.     

Localized protein immobilization on microstructured polymeric surfaces for diagnostic applications

We demonstrate a localized protein immobilization method based on controlled physical adsorption on the three-phase boundary of an aqueous phase, a gas phase, and a polymeric material. By imprinting micrometer and sub-micrometer pillars onto a polymeric foil, superhydrophobic surfaces are fabricated. Those structures force the fluid locally into the Cassie–Baxter state and generate an artificial three-phase boundary at the edges of the imprinted pillars. First, fluorescence-labeled bovine serum albumin (BSA) and streptavidin dissolved in various buffer solutions are utilized to investigate protein adsorption on the structured surfaces. A stable adsorption of the respective protein on the three-phase boundary is observed. The following experiments use streptavidin adsorbed on the pillars to immobilize biotinylated antibodies for analyte detection. The pillars are passivated with an excess concentration of BSA to reduce nonspecific protein adsorption. Implemented in a lab-on-a-chip device, the proposed immobilization method is utilized in a sandwich assay to detect the inflammation marker C-reactive protein in human serum, showing the potential of this immobilization method for diagnostic applications. The method overcomes laborious procedures to immobilize proteins on thermoplastic materials, which enables the fast transfer of point-of-care applications from research to commercial scale.     

Customising an antibody leukocyte capture microarray for systemic lupus erythematosus: beyond biomarker discovery

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has heterogeneous clinical manifestation with diverse patterns of organ involvement, autoantibody profiles and varying degrees of severity of disease. Research and clinical experience indicate that different subtypes of SLE patients will likely benefit from more tailored treatment regimes, but we currently lack a fast and objective test with high enough sensitivity to enable us to perform such sub-grouping for clinical use. In this article, we review how proteomic technologies could be used as such an objective test. In particular, we extensively review many leukocyte surface markers that are known to have an association with the pathogenesis of SLE, and we discuss how these markers can be used in the further development of a novel SLE-specific antibody leukocyte capture microarray. In addition, we review some bioinformatics challenges and current methods for using the data generated by these cell-capture microarrays in clinical use. In a broader context, we hope our experience in developing a disease specific cell-capture microarray for clinical application can be a guide to other proteomic practitioners who intend to extend their technologies to develop clinical diagnostic and prognostic tests for complex diseases.     

On the applicability of diagnostic criteria for the diagnosis of rheumatoid arthritis in an expert system

-2/ is a medical expert system developed to assist in the differential diagnosis of rheumatic diseases. Based on fuzzy set theory and fuzzy logic, it supports the formalization of vague and uncertain medical information (i.e., medical entities and relationships between them) and draws justifiable conclusions from these imprecise data. Given a patient's finding patter, -2 provides confirmed and excluded diagnoses, diagnostic hypotheses, and suggestions for further examinations. The knowledge base of -2 has been designed to contain simple finding/disease relationships as well as diagnostic rules of high complexity to confirm or hypothesize disease. We shall present results obtained with 300 clinical cases from a hospital for rheumatic diseases. Different rules for the diagnosis of rheumatoid arthritis based upon classification criteria issued by the American Rheumatism Association were tested against each other. That diagnostic rule which had shown the best results was then further improved by a rheumatology expert, which finally yielded a sensitivity of 83.3% and a specificity of 95.3%.