Polymerase chain reaction detection of LeishmaniaDNA in skin biopsy samples in Sri Lanka where the causative agent of cutaneous leishmaniasis is Leishmania donovani

Leishmania donovani is the known causative agent of both cutaneous (CL) and visceral leishmaniasis in Sri Lanka. CL is considered to be under-reported partly due to relatively poor sensitivity and specificity of microscopic diagnosis. We compared robustness of three previously described polymerase chain reaction (PCR) based methods to detectLeishmania DNA in 38 punch biopsy samples from patients presented with suspected lesions in 2010. Both, Leishmaniagenus-specific JW11/JW12 KDNA and LITSR/L5.8S internal transcribed spacer (ITS)1 PCR assays detected 92% (35/38) of the samples whereas a KDNA assay specific forL. donovani (LdF/LdR) detected only 71% (27/38) of samples. All positive samples showed a L. donovani banding pattern upon HaeIII ITS1 PCR-restriction fragment length polymorphism analysis. PCR assay specificity was evaluated in samples containing Mycobacterium tuberculosis, Mycobacterium leprae, and human DNA, and there was no cross-amplification in JW11/JW12 and LITSR/L5.8S PCR assays. The LdF/LdR PCR assay did not amplify M. leprae or human DNA although 500 bp and 700 bp bands were observed in M. tuberculosis samples. In conclusion, it was successfully shown in this study that it is possible to diagnose Sri Lankan CL with high accuracy, to genus and species identification, using Leishmania DNA PCR assays.

Assessment of airborne microorganisms by real-time PCR: optimistic findings and research challenges

Most airborne microorganisms are natural components of our ecosystem. Soil, vegetation and animals, including humans, are sources for aerial release of these living or dead cells. In the past, assessment of airborne microorganisms was mainly restricted to occupational health concerns. Indeed, in several occupations, exposure to very high concentrations of non-infectious airborne bacteria and fungi, result in allergenic, toxic or irritant reactions. Recently, the threat of bioterrorism and pandemics have highlighted the urgent need to increase knowledge of bioaerosol ecology. More fundamentally, airborne bacterial and fungal communities begin to draw much more consideration from environmental microbiologists, who have neglected this area for a long time. This increased interest of scientists is to a great part due to the development and use of real-time PCR techniques to identify and quantify airborne microorganisms. Even if the advantages of the PCR technology are obvious, researchers are confronted with new problems. This review describes the methodological state of the art in bioaerosols field and emphasizes the future challenges and perspectives of the real-time PCR-based methods for airborne microorganism studies.

Intramolecular basis set superposition error effects on the planarity of benzene and other aromatic molecules: A solution to the problem

Recently, the surprising result that ab initio calculations on benzene and other planar arenes at correlated MP2, MP3, configuration interaction with singles and doubles (CISD), and coupled cluster with singles and doubles levels of theory using standard Pople’s basis sets yield nonplanar minima has been reported. The planar optimized structures turn out to be transition states presenting one or more large imaginary frequencies, whereas single-determinant-based methods lead to the expected planar minima and no imaginary frequencies. It has been suggested that such anomalous behavior can be originated by two-electron basis set incompleteness error. In this work, we show that the reported pitfalls can be interpreted in terms of intramolecular basis set superposition error (BSSE) effects, mostly between the C–H moieties constituting the arenes. We have carried out counterpoise-corrected optimizations and frequency calculations at the Hartree–Fock, B3LYP, MP2, and CISD levels of theory with several basis sets for a number of arenes. In all cases, correcting for intramolecular BSSE fixes the anomalous behavior of the correlated methods, whereas no significant differences are observed in the single-determinant case. Consequently, all systems studied are planar at all levels of theory. The effect of different intramolecular fragment definitions and the particular case of charged species, namely, cyclopentadienyl and indenyl anions, respectively, are also discussed

Extended bottom-up proteomics with secreted aspartic protease Sap9.

We investigate the benefits and experimental feasibility of approaches enabling the shift from short (1.7kDa on average) peptides in bottom-up proteomics to about twice longer (~3.2kDa on average) peptides in the so-called extended bottom-up proteomics. Candida albicans secreted aspartic protease Sap9 has been selected for evaluation as an extended bottom-up proteomic-grade enzyme due to its suggested dibasic cleavage specificity and ease of production. We report the extensive characterization of Sap9 specificity and selectivity revealing that protein cleavage by Sap9 most often occurs in the vicinity of proximal basic amino acids, and in select cases also at basic and hydrophobic residues. Sap9 is found to cleave a large variety of proteins in a relatively short, ~1h, period of time and it is efficient in a broad pH range, including slightly acidic, e. g., pH5.5, conditions. Importantly, the resulting peptide mixtures contain representative peptides primarily in the target 3-7kDa range. The utility and advantages of this enzyme in routine analysis of protein mixtures are demonstrated and the limitations are discussed. Overall, Sap9 has a potential to become an enzyme of choice in an extended bottom-up proteomics, which is technically ready to complement the traditional bottom-up proteomics for improved targeted protein structural analysis and expanded proteome coverage. BIOLOGICAL SIGNIFICANCE: Advances in biological applications of mass spectrometry-based bottom-up proteomics are oftentimes limited by the extreme complexity of biological samples, e.g., proteomes or protein complexes. One of the reasons for it is in the complexity of the mixtures of enzymatically (most often using trypsin) produced short (<3kDa) peptides, which may exceed the analytical capabilities of liquid chromatography and mass spectrometry. Information on localization of protein modifications may also be affected by the small size of typically produced peptides. On the other hand, advances in high-resolution mass spectrometry and liquid chromatography have created an intriguing opportunity of improving proteome analysis by gradually increasing the size of enzymatically-derived peptides in MS-based bottom-up proteomics. Bioinformatics has already confirmed the envisioned advantages of such approach. The remaining bottle-neck is an enzyme that could produce longer peptides. Here, we report on the characterization of a possible candidate enzyme, Sap9, which may be considered for producing longer, e.g., 3-7kDa, peptides and lead to a development of extended bottom-up proteomics.

Gene finding in the chicken genome

Background: Despite the continuous production of genome sequence for a number of organisms,reliable, comprehensive, and cost effective gene prediction remains problematic. This is particularlytrue for genomes for which there is not a large collection of known gene sequences, such as therecently published chicken genome. We used the chicken sequence to test comparative andhomology-based gene-finding methods followed by experimental validation as an effective genomeannotation method.Results: We performed experimental evaluation by RT-PCR of three different computational genefinders, Ensembl, SGP2 and TWINSCAN, applied to the chicken genome. A Venn diagram wascomputed and each component of it was evaluated. The results showed that de novo comparativemethods can identify up to about 700 chicken genes with no previous evidence of expression, andcan correctly extend about 40% of homology-based predictions at the 5' end.Conclusions: De novo comparative gene prediction followed by experimental verification iseffective at enhancing the annotation of the newly sequenced genomes provided by standardhomology-based methods.

Invasive candidiasis as a cause of sepsis in the critically ill patient.

Invasive fungal infections are an increasingly frequent etiology of sepsis in critically ill patients causing substantial morbidity and mortality. Candida species are by far the predominant agent of fungal sepsis accounting for 10% to 15% of health-care associated infections, about 5% of all cases of severe sepsis and septic shock and are the fourth most common bloodstream isolates in the United States. One-third of all episodes of candidemia occur in the intensive care setting. Early diagnosis of invasive candidiasis is critical in order to initiate antifungal agents promptly. Delay in the administration of appropriate therapy increases mortality. Unfortunately, risk factors, clinical and radiological manifestations are quite unspecific and conventional culture methods are suboptimal. Non-culture based methods (such as mannan, anti-mannan, β-d-glucan, and polymerase chain reaction) have emerged but remain investigational or require additional testing in the ICU setting. Few prophylactic or pre-emptive studies have been performed in critically ill patients. They tended to be underpowered and their clinical usefulness remains to be established under most circumstances. The antifungal armamentarium has expanded considerably with the advent of lipid formulations of amphotericin B, the newest triazoles and the echinocandins. Clinical trials have shown that the triazoles and echinocandins are efficacious and well tolerated antifungal therapies. Clinical practice guidelines for the management of invasive candidiasis have been published by the European Society for Clinical Microbiology and Infectious Diseases and the Infectious Diseases Society of North America.

Allowing for heterogeneity in the decomposition of measures of inequality in health

This paper shows how recently developed regression-based methods for the decomposition ofhealth inequality can be extended to incorporate heterogeneity in the responses of health to the explanatory variables. We illustrate our method with an application to the GHQ measure of psychological well-being taken from the British Household Panel Survey. The results suggest that there is an important degree of heterogeneity in the association of health to explanatory variables across birth cohorts and genders which, in turn, accounts for a substantial percentage of the inequality in observed health.

On the performance of small-area estimators: Fixed vs. random area parameters

Most methods for small-area estimation are based on composite estimators derived from design- or model-based methods. A composite estimator is a linear combination of a direct and an indirect estimator with weights that usually depend on unknown parameters which need to be estimated. Although model-based small-area estimators are usually based on random-effects models, the assumption of fixed effects is at face value more appropriate.Model-based estimators are justified by the assumption of random (interchangeable) area effects; in practice, however, areas are not interchangeable. In the present paper we empirically assess the quality of several small-area estimators in the setting in which the area effects are treated as fixed. We consider two settings: one that draws samples from a theoretical population, and another that draws samples from an empirical population of a labor force register maintained by the National Institute of Social Security (NISS) of Catalonia. We distinguish two types of composite estimators: a) those that use weights that involve area specific estimates of bias and variance; and, b) those that use weights that involve a common variance and a common squared bias estimate for all the areas. We assess their precision and discuss alternatives to optimizing composite estimation in applications.

Database construction and Peptide identification strategies for proteogenomic studies on sequenced genomes.

Since the advent of high-throughput DNA sequencing technologies, the ever-increasing rate at which genomes have been published has generated new challenges notably at the level of genome annotation. Even if gene predictors and annotation softwares are more and more efficient, the ultimate validation is still in the observation of predicted gene product( s). Mass-spectrometry based proteomics provides the necessary high throughput technology to show evidences of protein presence and, from the identified sequences, confirmation or invalidation of predicted annotations. We review here different strategies used to perform a MS-based proteogenomics experiment with a bottom-up approach. We start from the strengths and weaknesses of the different database construction strategies, based on different genomic information (whole genome, ORF, cDNA, EST or RNA-Seq data), which are then used for matching mass spectra to peptides and proteins. We also review the important points to be considered for a correct statistical assessment of the peptide identifications. Finally, we provide references for tools used to map and visualize the peptide identifications back to the original genomic information.

Label-free cytotoxicity screening assay by digital holographic microscopy

Abstract We introduce a label-free technology based on digital holographic microscopy (DHM) with applicability for screening by imaging, and we demonstrate its capability for cytotoxicity assessment using mammalian living cells. For this first high content screening compatible application, we automatized a digital holographic microscope for image acquisition of cells using commercially available 96-well plates. Data generated through both label-free DHM imaging and fluorescence-based methods were in good agreement for cell viability identification and a Z'-factor close to 0.9 was determined, validating the robustness of DHM assay for phenotypic screening. Further, an excellent correlation was obtained between experimental cytotoxicity dose-response curves and known IC values for different toxic compounds. For comparable results, DHM has the major advantages of being label free and close to an order of magnitude faster than automated standard fluorescence microscopy.

SwissParam: a fast force field generation tool for small organic molecules.

The drug discovery process has been deeply transformed recently by the use of computational ligand-based or structure-based methods, helping the lead compounds identification and optimization, and finally the delivery of new drug candidates more quickly and at lower cost. Structure-based computational methods for drug discovery mainly involve ligand-protein docking and rapid binding free energy estimation, both of which require force field parameterization for many drug candidates. Here, we present a fast force field generation tool, called SwissParam, able to generate, for arbitrary small organic molecule, topologies, and parameters based on the Merck molecular force field, but in a functional form that is compatible with the CHARMM force field. Output files can be used with CHARMM or GROMACS. The topologies and parameters generated by SwissParam are used by the docking software EADock2 and EADock DSS to describe the small molecules to be docked, whereas the protein is described by the CHARMM force field, and allow them to reach success rates ranging from 56 to 78%. We have also developed a rapid binding free energy estimation approach, using SwissParam for ligands and CHARMM22/27 for proteins, which requires only a short minimization to reproduce the experimental binding free energy of 214 ligand-protein complexes involving 62 different proteins, with a standard error of 2.0 kcal mol(-1), and a correlation coefficient of 0.74. Together, these results demonstrate the relevance of using SwissParam topologies and parameters to describe small organic molecules in computer-aided drug design applications, together with a CHARMM22/27 description of the target protein. SwissParam is available free of charge for academic users at www.swissparam.ch.

Variability in urinary oxalate measurements between six international laboratories.

BACKGROUND: Hyperoxaluria is a major risk factor for kidney stone formation. Although urinary oxalate measurement is part of all basic stone risk assessment, there is no standardized method for this measurement. METHODS: Urine samples from 24-h urine collection covering a broad range of oxalate concentrations were aliquoted and sent, in duplicates, to six blinded international laboratories for oxalate, sodium and creatinine measurement. In a second set of experiments, ten pairs of native urine and urine spiked with 10 mg/L of oxalate were sent for oxalate measurement. Three laboratories used a commercially available oxalate oxidase kit, two laboratories used a high-performance liquid chromatography (HPLC)-based method and one laboratory used both methods. RESULTS: Intra-laboratory reliability for oxalate measurement expressed as intraclass correlation coefficient (ICC) varied between 0.808 [95% confidence interval (CI): 0.427-0.948] and 0.998 (95% CI: 0.994-1.000), with lower values for HPLC-based methods. Acidification of urine samples prior to analysis led to significantly higher oxalate concentrations. ICC for inter-laboratory reliability varied between 0.745 (95% CI: 0.468-0.890) and 0.986 (95% CI: 0.967-0.995). Recovery of the 10 mg/L oxalate-spiked samples varied between 8.7 ± 2.3 and 10.7 ± 0.5 mg/L. Overall, HPLC-based methods showed more variability compared to the oxalate oxidase kit-based methods. CONCLUSIONS: Significant variability was noted in the quantification of urinary oxalate concentration by different laboratories, which may partially explain the differences of hyperoxaluria prevalence reported in the literature. Our data stress the need for a standardization of the method of oxalate measurement.

Mult-class differentiation of cannabis seedlings in a forensic context

This article presents an experimental study about the classification ability of several classifiers for multi-classclassification of cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland lawenforcement authorities regularly ask forensic laboratories to determinate the chemotype of a seized cannabisplant and then to conclude if the plantation is legal or not. This classification is mainly performed when theplant is mature as required by the EU official protocol and then the classification of cannabis seedlings is a timeconsuming and costly procedure. A previous study made by the authors has investigated this problematic [1]and showed that it is possible to differentiate between drug type (illegal) and fibre type (legal) cannabis at anearly stage of growth using gas chromatography interfaced with mass spectrometry (GC-MS) based on therelative proportions of eight major leaf compounds. The aims of the present work are on one hand to continueformer work and to optimize the methodology for the discrimination of drug- and fibre type cannabisdeveloped in the previous study and on the other hand to investigate the possibility to predict illegal cannabisvarieties. Seven classifiers for differentiating between cannabis seedlings are evaluated in this paper, namelyLinear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), Nearest NeighbourClassification (NNC), Learning Vector Quantization (LVQ), Radial Basis Function Support Vector Machines(RBF SVMs), Random Forest (RF) and Artificial Neural Networks (ANN). The performance of each method wasassessed using the same analytical dataset that consists of 861 samples split into drug- and fibre type cannabiswith drug type cannabis being made up of 12 varieties (i.e. 12 classes). The results show that linear classifiersare not able to manage the distribution of classes in which some overlap areas exist for both classificationproblems. Unlike linear classifiers, NNC and RBF SVMs best differentiate cannabis samples both for 2-class and12-class classifications with average classification results up to 99% and 98%, respectively. Furthermore, RBFSVMs correctly classified into drug type cannabis the independent validation set, which consists of cannabisplants coming from police seizures. In forensic case work this study shows that the discrimination betweencannabis samples at an early stage of growth is possible with fairly high classification performance fordiscriminating between cannabis chemotypes or between drug type cannabis varieties.

Refining abacavir hypersensitivity diagnoses using a structured clinical assessment and genetic testing in the Swiss HIV Cohort Study.

BACKGROUND: We aimed to assess the value of a structured clinical assessment and genetic testing for refining the diagnosis of abacavir hypersensitivity reactions (ABC-HSRs) in a routine clinical setting. METHODS: We performed a diagnostic reassessment using a structured patient chart review in individuals who had stopped ABC because of suspected HSR. Two HIV physicians blinded to the human leukocyte antigen (HLA) typing results independently classified these individuals on a scale between 3 (ABC-HSR highly likely) and -3 (ABC-HSR highly unlikely). Scoring was based on symptoms, onset of symptoms and comedication use. Patients were classified as clinically likely (mean score > or =2), uncertain (mean score > or = -1 and < or = 1) and unlikely (mean score < or = -2). HLA typing was performed using sequence-based methods. RESULTS: From 131 reassessed individuals, 27 (21%) were classified as likely, 43 (33%) as unlikely and 61 (47%) as uncertain ABC-HSR. Of the 131 individuals with suspected ABC-HSR, 31% were HLA-B*5701-positive compared with 1% of 140 ABC-tolerant controls (P < 0.001). HLA-B*5701 carriage rate was higher in individuals with likely ABC-HSR compared with those with uncertain or unlikely ABC-HSR (78%, 30% and 5%, respectively, P < 0.001). Only six (7%) HLA-B*5701-negative individuals were classified as likely HSR after reassessment. CONCLUSIONS: HLA-B*5701 carriage is highly predictive of clinically diagnosed ABC-HSR. The high proportion of HLA-B*5701-negative individuals with minor symptoms among individuals with suspected HSR indicates overdiagnosis of ABC-HSR in the era preceding genetic screening. A structured clinical assessment and genetic testing could reduce the rate of inappropriate ABC discontinuation and identify individuals at high risk for ABC-HSR.

Three-dimensional radiobiological dosimetry of kidneys for treatment planning in peptide receptor radionuclide therapy.

PURPOSE: Peptide receptor radionuclide therapy (PRRT) delivers high absorbed doses to kidneys and may lead to permanent nephropathy. Reliable dosimetry of kidneys is thus critical for safe and effective PRRT. The aim of this work was to assess the feasibility of planning PRRT based on 3D radiobiological dosimetry (3D-RD) in order to optimize both the amount of activity to administer and the fractionation scheme, while limiting the absorbed dose and the biological effective dose (BED) to the renal cortex. METHODS: Planar and SPECT data were available for a patient examined with (111)In-DTPA-octreotide at 0.5 (planar only), 4, 24, and 48 h post-injection. Absorbed dose and BED distributions were calculated for common therapeutic radionuclides, i.e., (111)In, (90)Y and (177)Lu, using the 3D-RD methodology. Dose-volume histograms were computed and mean absorbed doses to kidneys, renal cortices, and medullae were compared with results obtained using the MIRD schema (S-values) with the multiregion kidney dosimetry model. Two different treatment planning approaches based on (1) the fixed absorbed dose to the cortex and (2) the fixed BED to the cortex were then considered to optimize the activity to administer by varying the number of fractions. RESULTS: Mean absorbed doses calculated with 3D-RD were in good agreement with those obtained with S-value-based SPECT dosimetry for (90)Y and (177)Lu. Nevertheless, for (111)In, differences of 14% and 22% were found for the whole kidneys and the cortex, respectively. Moreover, the authors found that planar-based dosimetry systematically underestimates the absorbed dose in comparison with SPECT-based methods, up to 32%. Regarding the 3D-RD-based treatment planning using a fixed BED constraint to the renal cortex, the optimal number of fractions was found to be 3 or 4, depending on the radionuclide administered and the value of the fixed BED. Cumulative activities obtained using the proposed simulated treatment planning are compatible with real activities administered to patients in PRRT. CONCLUSIONS: The 3D-RD treatment planning approach based on the fixed BED was found to be the method of choice for clinical implementation in PRRT by providing realistic activity to administer and number of cycles. While dividing the activity in several cycles is important to reduce renal toxicity, the clinical outcome of fractionated PRRT should be investigated in the future.