Fertility and marital status in adults with childhood onset epilepsy: A population-based cohort study.

OBJECTIVE: Our objective was to explore the association of childhood onset epilepsy (COE) and clinical factors on marital status and fertility in adulthood. METHODS: We identified a population-based cohort of 307 individuals with COE treated in the Tampere University Hospital district with an inception date of December 31, 1992. A matched reference cohort of 1244 individuals without COE was established as a random sample of the population in the study area through the Population Register Center (PRC). The PRC also provided data on marriages and offspring up to 2018. Fertility and marriage analysis was done by calculating the time until first child and marriage. Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated with Cox regression for follow-up spanning up to January 2018. RESULTS: Patients with COE had lower fertility rates (32.2% vs 57.3% any offspring, HR = 0.47, 95% CI = 0.38-0.58) and fewer marriages (28.3% vs 49.7% ever married, HR = 0.49, 95% CI = 0.39-0.61) than the referents without COE during 25-year follow-up. The largest impact was in patients with COE who had any disability (10.1% any offspring, HR = 0.20, 95% CI = 0.10-0.41; 6.5% ever married, HR = 0.11, 95% CI = 0.06-0.21), symptomatic etiology of epilepsy (13.1%, HR = 0.18, 95% CI = 0.11-0.31; 12.1%, HR = 0.21, 95% CI = 0.12-0.36), onset of epilepsy before 2 years of age (HR = 0.20, 95% CI = 0.12-0.31; HR = 0.29, 95% CI = 0.18-0.46), and high seizure frequency after start of treatment (HR = 0.13, 95% CI = 0.06-0.28; HR = 0.20, 95% CI = 0.10-0.41). Patients with COE without any disabilities had only slightly lowered fertility (HR = 0.76, 95% CI = 0.61-0.95) and a nonsignificant reduction in marriages (HR = 0.80, 95% CI = 0.64-1.02). SIGNIFICANCE: COE was associated with a lower chance of finding a partner at adulthood and having fewer children. The extent of such effect varied between patient subgroups.

Direct Reflectance Measurements from Drones: Sensor Absolute Radiometric Calibration and System Tests for Forest Reflectance Characterization.

Drone-based remote sensing has evolved rapidly in recent years. Miniaturized hyperspectral imaging sensors are becoming more common as they provide more abundant information of the object compared to traditional cameras. Reflectance is a physically defined object property and therefore often preferred output of the remote sensing data capture to be used in the further processes. Absolute calibration of the sensor provides a possibility for physical modelling of the imaging process and enables efficient procedures for reflectance correction. Our objective is to develop a method for direct reflectance measurements for drone-based remote sensing. It is based on an imaging spectrometer and irradiance spectrometer. This approach is highly attractive for many practical applications as it does not require in situ reflectance panels for converting the sensor radiance to ground reflectance factors. We performed SI-traceable spectral and radiance calibration of a tuneable Fabry-Pérot Interferometer -based (FPI) hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). The camera represents novel technology by collecting 2D format hyperspectral image cubes using time sequential spectral scanning principle. The radiance accuracy of different channels varied between ±4% when evaluated using independent test data, and linearity of the camera response was on average 0.9994. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI. The drone-based direct reflectance measurement system showed promising results with imagery collected over Wytham Forest (Oxford, UK).

Epilepsy, excess deaths and years of life lost from external causes.

We systematically quantified excess mortality in epilepsy patients by cause of death using the population-attributable fraction and epilepsy-attributable years of potential life lost (YPLL) by age 75 years at ages 15 and over. We updated and undertook a re-review of mortality studies from our previous systematic review following PRISMA guidelines to identify cohort studies of general epilepsy populations reporting a relative risk (RR) of death by cause relative to the background rates in the population. Studies on epilepsy prevalence were identified through published reviews. Country-specific mortality figures were obtained from the WHO World Mortality Database. We performed a pooled analysis with the DerSimonian-Laird random effects method. In countries with very high Human Development Indices, epilepsy contributed to 0.5-1.1 % of all deaths in the total population. Among external causes, suicides (RR 2.9, 95 % confidence interval 2.2-3.8; I(2) 52 %) were the major contributor to YPLL, corresponding to 6.7 % and 4.2 % of excess YPLL due to epilepsy in the United States (US) and in the United Kingdom (UK) in 2010, with 541 (346-792) and 44 (28-65) excess suicide cases, respectively. Fatal accidental falls were more common, with 813 (610-1064) and 95 (71-125) excess deaths in the US and in the UK, but these caused only 2.0 % of excess YPLL as they occurred in older age groups. Suicides were the most important external cause of death in epilepsy patients in terms of excess YPLL, whereas other external causes were either more common in older ages or caused less excess deaths.

Using Peptide-Level Proteomics Data for Detecting Differentially Expressed Proteins.

The expression of proteins can be quantified in high-throughput means using different types of mass spectrometers. In recent years, there have emerged label-free methods for determining protein abundance. Although the expression is initially measured at the peptide level, a common approach is to combine the peptide-level measurements into protein-level values before differential expression analysis. However, this simple combination is prone to inconsistencies between peptides and may lose valuable information. To this end, we introduce here a method for detecting differentially expressed proteins by combining peptide-level expression-change statistics. Using controlled spike-in experiments, we show that the approach of averaging peptide-level expression changes yields more accurate lists of differentially expressed proteins than does the conventional protein-level approach. This is particularly true when there are only few replicate samples or the differences between the sample groups are small. The proposed technique is implemented in the Bioconductor package PECA, and it can be downloaded from http://www.bioconductor.org.

Cross-correlation of spectral count ranking to validate quantitative proteome measurements.

The measurement of change in biological systems through protein quantification is a central theme in modern biosciences and medicine. Label-free MS-based methods have greatly increased the ease and throughput in performing this task. Spectral counting is one such method that uses detected MS2 peptide fragmentation ions as a measure of the protein amount. The method is straightforward to use and has gained widespread interest. Additionally reports on new statistical methods for analyzing spectral count data appear at regular intervals, but a systematic evaluation of these is rarely seen. In this work, we studied how similar the results are from different spectral count data analysis methods, given the same biological input data. For this, we chose the algorithms Beta Binomial, PLGEM, QSpec, and PepC to analyze three biological data sets of varying complexity. For analyzing the capability of the methods to detect differences in protein abundance, we also performed controlled experiments by spiking a mixture of 48 human proteins in varying concentrations into a yeast protein digest to mimic biological fold changes. In general, the agreement of the analysis methods was not particularly good on the proteome-wide scale, as considerable differences were found between the different algorithms. However, we observed good agreements between the methods for the top abundance changed proteins, indicating that for a smaller fraction of the proteome changes are measurable, and the methods may be used as valuable tools in the discovery-validation pipeline when applying a cross-validation approach as described here. Performance ranking of the algorithms using samples of known composition showed PLGEM to be superior, followed by Beta Binomial, PepC, and QSpec. Similarly, the normalized versions of the same method, when available, generally outperformed the standard ones. Statistical detection of protein abundance differences was strongly influenced by the number of spectra acquired for the protein and, correspondingly, its molecular mass.

Long-term mortality risk by cause of death in newly diagnosed patients with epilepsy in Finland: a nationwide register-based study.

To estimate long-term mortality by cause of death in a nationwide, register-based cohort of newly diagnosed patients with epilepsy (PWE). All noninstitutionalized Finnish PWE aged 10-74 years (n = 10,818) eligible for reimbursement for antiepileptic medication for the first time between 1990 and 1994 were identified in the database of Social Insurance Institution of Finland. Mortality was compared against a population-based reference cohort (n = 43,894). Hazard ratios (HR) and their 95 % confidence intervals (95 % CI) during a follow-up of 18 years were estimated using proportional hazards modeling. Potential years of life lost (PYLL) and excess fraction of causes of death attributable to epilepsy were estimated. PWE contributed 137,610 person-years of observation and there were 3,558 deaths. Mortality remained elevated up to 18 years post-diagnosis (HR 3.21, 95 % CI 3.07-3.35). Ischemic heart disease mortality in PWE was two-fold (HR 2.31, 95 % CI 2.09-2.54), and remained constantly elevated during entire follow-up in both men and women. Most premature mortality in terms of PYLL was attributable to brain cancer (17 %), other cancers (15 %), ischemic heart disease (11 %), as well as cerebrovascular diseases (10 %). The percentage of deaths in PWE statistically attributable to epilepsy was 3.9 % for accidents, 3.4 % for alcohol-related diseases, and 1.6 % for suicides. PWE had substantial excess mortality from non-communicable diseases, which did not disappear by 18 years. Diseases of the circulatory system and cancers, especially brain cancer, were the most important causes of death almost regardless of the mortality indicator.

Autoimmune and immune-mediated inflammatory diseases after exposure to acid-suppressive medication: A systematic review and meta-analysis.

BACKGROUND: Pharmacovigilance risk signals have proposed a relationship between the use of acid-suppressive medications and the development of certain autoimmune and immune-mediated inflammatory diseases. OBJECTIVE: A systematic review and a meta-analysis was performed. METHODS: We reviewed MEDLINE (Ovid) and Scopus for comparative observational studies between these diseases and previous exposure to proton-pump inhibitors (PPI), H2-receptor antagonists (H2RA), and antacids. The protocol was registered on the PROSPERO database (CRD42020192715). RESULTS: From 3,191 citations, 25 articles were eligible and covered 16 diseases. Microscopic colitis (MC) was studied the most (7 studies). In a random-effects meta-analysis, there was low certainty evidence (GRADE approach) of a non-significant relationship between exposure to any PPIs and MC (meta-OR 3.28, 95% CI 0.98-11.0, I2 98.2%, six studies, 4,436 PPI-exposed MC patients). Moderate certainty evidence pointed towards large odds of collagenous colitis after exposure to lansoprazole (meta-OR 14.5, 95% CI 9.37-22.3, I2 10.2%, three studies, 1,725 lansoprazole-exposed patients). After PPI exposure, the risk of rheumatoid arthritis was slightly increased based on low certainty evidence from two cohort studies totaling 475 diagnoses (meta-RR 1.62, 95% CI 1.12-2.34, I2 34.5%). CONCLUSIONS: In patients with MC, it would be reasonable to carefully review the indication of PPI, especially in CC patients using lansoprazole.

Psychiatric medications and the risk of autoimmune and immune-mediated inflammatory diseases: A systematic review and meta-analysis of observational studies.

BACKGROUND: Pharmacovigilance reports have suggested that certain commonly used medications may trigger autoimmune diseases (ADs) and immune-mediated inflammatory diseases (IMIDs). We systematically reviewed the literature to evaluate whether psychiatric medication use is associated with ADs and IMIDs. METHODS: The protocol was registered in PROSPERO (CRD42022296524) before the start of the study. We searched Medline Ovid and Scopus up to November 28th, 2021, for comparative studies, with any psychiatric medication as exposure and ADs and IMIDs as outcomes. Meta-analysis was performed using DerSimonian-Laird random-effects modeling. The PRISMA 2020 guidelines were followed in reporting. Study-level risk of bias was assessed using the Newcastle-Ottawa Scale, and the overall certainty of evidence using GRADE. RESULTS: There were 7,265 citations from which 31 studies were eligible, all from high-income countries, covering 15 distinct immune diseases. The evidence for the association between selective serotonin reuptake inhibitor (SSRI) use and higher risk of microscopic colitis (meta-OR 2.60, 95% CI 1.05-6.39, I2 97.5%, 6 studies) was of low certainty. A subgroup analysis by the histological type of microscopic colitis showed a statistically significant association only with lymphocytic colitis (meta-OR 2.88, 95% CI 2.60-3.18, I2 00.00%, three studies). In two case-control studies, SSRI use had no significant association with psoriasis (meta-OR 0.80, 95% CI 0.58-1.10, I2 82.4%). The risk of acute pancreatitis was slightly increased with exposure to SSRIs (meta-OR 1.13, 95% CI 1.01-1.26, I2 00.0%), as was the risk of bullous pemphigoid after exposure to antipsychotics (meta-OR 1.79, 95% CI 1.17-2.73, I2 0%). CONCLUSIONS: We reviewed the literature on whether psychiatric medications associate with the risk of ADs and IMIDs and concluded that, despite several signals, the credibility of evidence remains low at best. Prospective cohort studies would be needed as the next step to confirm the suggestions of increased risk.

Reporting of costs and economic impacts in randomized trials of de-implementation interventions for low-value care: a systematic scoping review.

BACKGROUND: De-implementation of low-value care can increase health care sustainability. We evaluated the reporting of direct costs of de-implementation and subsequent change (increase or decrease) in health care costs in randomized trials of de-implementation research. METHODS: We searched MEDLINE and Scopus databases without any language restrictions up to May 2021. We conducted study screening and data extraction independently and in duplicate. We extracted information related to study characteristics, types and characteristics of interventions, de-implementation costs, and impacts on health care costs. We assessed risk of bias using a modified Cochrane risk-of-bias tool. RESULTS: We screened 10,733 articles, with 227 studies meeting the inclusion criteria, of which 50 included information on direct cost of de-implementation or impact of de-implementation on health care costs. Studies were mostly conducted in North America (36%) or Europe (32%) and in the primary care context (70%). The most common practice of interest was reduction in the use of antibiotics or other medications (74%). Most studies used education strategies (meetings, materials) (64%). Studies used either a single strategy (52%) or were multifaceted (48%). Of the 227 eligible studies, 18 (8%) reported on direct costs of the used de-implementation strategy; of which, 13 reported total costs, and 12 reported per unit costs (7 reported both). The costs of de-implementation strategies varied considerably. Of the 227 eligible studies, 43 (19%) reported on impact of de-implementation on health care costs. Health care costs decreased in 27 studies (63%), increased in 2 (5%), and were unchanged in 14 (33%). CONCLUSION: De-implementation randomized controlled trials typically did not report direct costs of the de-implementation strategies (92%) or the impacts of de-implementation on health care costs (81%). Lack of cost information may limit the value of de-implementation trials to decision-makers. TRIAL REGISTRATION: OSF (Open Science Framework): https://osf.io/ueq32 .

Effects of remdesivir in patients hospitalised with COVID-19: a systematic review and individual patient data meta-analysis of randomised controlled trials.

BACKGROUND: Interpretation of the evidence from randomised controlled trials (RCTs) of remdesivir in patients treated in hospital for COVID-19 is conflicting. We aimed to assess the benefits and harms of remdesivir compared with placebo or usual care in these patients, and whether treatment effects differed between prespecified patient subgroups. METHODS: For this systematic review and meta-analysis, we searched PubMed, Embase, the Cochrane COVID-19 trial registry, ClinicalTrials.gov, the International Clinical Trials Registry Platform, and preprint servers from Jan 1, 2020, until April 11, 2022, for RCTs of remdesivir in adult patients hospitalised with COVID-19, and contacted the authors of eligible trials to request individual patient data. The primary outcome was all-cause mortality at day 28 after randomisation. We used multivariable hierarchical regression-adjusting for respiratory support, age, and enrollment period-to investigate effect modifiers. This study was registered with PROSPERO, CRD42021257134. FINDINGS: Our search identified 857 records, yielding nine RCTs eligible for inclusion. Of these nine eligible RCTs, individual data were provided for eight, covering 10 480 patients hospitalised with COVID-19 (99% of such patients included in such RCTs worldwide) recruited between Feb 6, 2020, and April 1, 2021. Within 28 days of randomisation, 662 (12·5%) of 5317 patients assigned to remdesivir and 706 (14·1%) of 5005 patients assigned to no remdesivir died (adjusted odds ratio [aOR] 0·88, 95% CI 0·78-1·00, p=0·045). We found evidence for a credible subgroup effect according to respiratory support at baseline (pinteraction=0·019). Of patients who were ventilated-including those who received high-flow oxygen-253 (30·0%) of 844 patients assigned to remdesivir died compared with 241 (28·5%) of 846 patients assigned to no remdesivir (aOR 1·10 [0·88-1·38]; low-certainty evidence). Of patients who received no oxygen or low-flow oxygen, 409 (9·1%) of 4473 patients assigned to remdesivir died compared with 465 (11·2%) of 4159 patients assigned to no remdesivir (0·80 [0·70-0·93]; high-certainty evidence). No credible subgroup effect was found for time to start of remdesivir after symptom onset, age, presence of comorbidities, enrolment period, or corticosteroid use. Remdesivir did not increase the frequency of severe or serious adverse events. INTERPRETATION: This individual patient data meta-analysis showed that remdesivir reduced mortality in patients hospitalised with COVID-19 who required no or conventional oxygen support, but was underpowered to evaluate patients who were ventilated when receiving remdesivir. The effect size of remdesivir in patients with more respiratory support or acquired immunity and the cost-effectiveness of remdesivir remain to be further elucidated. FUNDING: EU-RESPONSE.

Exploring the transcriptomic variation caused by the Finnish founder mutation of lysinuric protein intolerance (LPI).

Lysinuric protein intolerance (LPI) is an autosomal recessive disorder caused by mutations in cationic amino acid transporter gene SLC7A7. Although all Finnish patients share the same homozygous mutation, their clinical manifestations vary greatly. The symptoms range from failure to thrive, protein aversion, anemia and hyperammonaemia, to immunological abnormalities, nephropathy and pulmonary alveolar proteinosis. To unravel the molecular mechanisms behind those symptoms not explained directly by the primary mutation, gene expression profiles of LPI patients were studied using genome-wide microarray technology. As a result, we discovered 926 differentially-expressed genes, including cationic and neutral amino acid transporters. The functional annotation analysis revealed a significant accumulation of such biological processes as inflammatory response, immune system processes and apoptosis. We conclude that changes in the expression of genes other than SLC7A7 may be linked to the various symptoms of LPI, indicating a complex interplay between amino acid transporters and various cellular processes.

Mortality by clinical characteristics in a tertiary care cohort of adult patients with chronic epilepsy.

The authors evaluated the contribution of various clinical characteristics to mortality risk and underlying causes of death among all adult patients with epilepsy seen at the Department of Neurology, Oulu University Hospital in Finland during 1996 and 1997. Hazard ratios (HRs) for mortality in 1998-2006 relative to a population-based reference cohort were estimated using Cox modeling, with adjustment for age and gender. The HR for total mortality was 2.66 (95% confidence interval [CI] 2.09-3.39). Infectious etiology of epilepsy (HR 5.77, 95% CI 2.52-13.2) and a seizure frequency of ≥1 per month (HR 4.42, 95% CI 3.00-6.52) related to high risks of death. Cancer (21%), ischemic heart disease (15%), and accidents (12%) caused most of the potential years of life lost. Despite recent advances in treatment of epilepsy and improved seizure control, chronic epilepsy still carries a substantially increased risk of death.

Chronic unilateral groin pain in a young patient who injects drugs: a case report of needle fragment retentions.

BACKGROUND: Subcutaneously retained needle fragments in people who inject drugs (PWIDs) are a possible cause of local symptoms, most commonly pain and infections. It remains unknown how common retained needle fragments are among PWIDs. CASE PRESENTATION: A young PWID consulted a primary care physician due to chronic left-sided groin pain. The patient suspected retention of a broken needle as the cause. She had used a re-used needle 3 months earlier. A plain pelvic radiograph confirmed a needle fragment in the patient's left groin, and a computed tomography scan located it adjacent to the femoral artery and vein. Another asymptomatic needle fragment was found in the right groin. CONCLUSION: Needle fragments are possible causes of local symptoms among PWIDs. The clinical examination presents a potential risk of needlestick injury to the examiner, especially because patients may not be aware of all needle fragments as some are asymptomatic.

Effect of remdesivir post hospitalization for COVID-19 infection from the randomized SOLIDARITY Finland trial.

We report the first long-term follow-up of a randomized trial (NCT04978259) addressing the effects of remdesivir on recovery (primary outcome) and other patient-important outcomes one year after hospitalization resulting from COVID-19. Of the 208 patients recruited from 11 Finnish hospitals, 198 survived, of whom 181 (92%) completed follow-up. At one year, self-reported recovery occurred in 85% in remdesivir and 86% in standard of care (SoC) (RR 0.94, 95% CI 0.47-1.90). We infer no convincing difference between remdesivir and SoC in quality of life or symptom outcomes (p > 0.05). Of the 21 potential long-COVID symptoms, patients reported moderate/major bother from fatigue (26%), joint pain (22%), and problems with memory (19%) and attention/concentration (18%). In conclusion, after a one-year follow-up of hospitalized patients, one in six reported they had not recovered well from COVID-19. Our results provide no convincing evidence of remdesivir benefit, but wide confidence intervals included possible benefit and harm.

Randomized controlled trials in de-implementation research: a systematic scoping review.

BACKGROUND: Healthcare costs are rising, and a substantial proportion of medical care is of little value. De-implementation of low-value practices is important for improving overall health outcomes and reducing costs. We aimed to identify and synthesize randomized controlled trials (RCTs) on de-implementation interventions and to provide guidance to improve future research. METHODS: MEDLINE and Scopus up to May 24, 2021, for individual and cluster RCTs comparing de-implementation interventions to usual care, another intervention, or placebo. We applied independent duplicate assessment of eligibility, study characteristics, outcomes, intervention categories, implementation theories, and risk of bias. RESULTS: Of the 227 eligible trials, 145 (64%) were cluster randomized trials (median 24 clusters; median follow-up time 305 days), and 82 (36%) were individually randomized trials (median follow-up time 274 days). Of the trials, 118 (52%) were published after 2010, 149 (66%) were conducted in a primary care setting, 163 (72%) aimed to reduce the use of drug treatment, 194 (85%) measured the total volume of care, and 64 (28%) low-value care use as outcomes. Of the trials, 48 (21%) described a theoretical basis for the intervention, and 40 (18%) had the study tailored by context-specific factors. Of the de-implementation interventions, 193 (85%) were targeted at physicians, 115 (51%) tested educational sessions, and 152 (67%) multicomponent interventions. Missing data led to high risk of bias in 137 (60%) trials, followed by baseline imbalances in 99 (44%), and deficiencies in allocation concealment in 56 (25%). CONCLUSIONS: De-implementation trials were mainly conducted in primary care and typically aimed to reduce low-value drug treatments. Limitations of current de-implementation research may have led to unreliable effect estimates and decreased clinical applicability of studied de-implementation strategies. We identified potential research gaps, including de-implementation in secondary and tertiary care settings, and interventions targeted at other than physicians. Future trials could be improved by favoring simpler intervention designs, better control of potential confounders, larger number of clusters in cluster trials, considering context-specific factors when planning the intervention (tailoring), and using a theoretical basis in intervention design. REGISTRATION: OSF Open Science Framework hk4b2.

Pripper: prediction of caspase cleavage sites from whole proteomes.

BACKGROUND: Caspases are a family of proteases that have central functions in programmed cell death (apoptosis) and inflammation. Caspases mediate their effects through aspartate-specific cleavage of their target proteins, and at present almost 400 caspase substrates are known. There are several methods developed to predict caspase cleavage sites from individual proteins, but currently none of them can be used to predict caspase cleavage sites from multiple proteins or entire proteomes, or to use several classifiers in combination. The possibility to create a database from predicted caspase cleavage products for the whole genome could significantly aid in identifying novel caspase targets from tandem mass spectrometry based proteomic experiments. RESULTS: Three different pattern recognition classifiers were developed for predicting caspase cleavage sites from protein sequences. Evaluation of the classifiers with quality measures indicated that all of the three classifiers performed well in predicting caspase cleavage sites, and when combining different classifiers the accuracy increased further. A new tool, Pripper, was developed to utilize the classifiers and predict the caspase cut sites from an arbitrary number of input sequences. A database was constructed with the developed tool, and it was used to identify caspase target proteins from tandem mass spectrometry data from two different proteomic experiments. Both known caspase cleavage products as well as novel cleavage products were identified using the database demonstrating the usefulness of the tool. Pripper is not restricted to predicting only caspase cut sites, but it gives the possibility to scan protein sequences for any given motif(s) and predict cut sites once a suitable cut site prediction model for any other protease has been developed. Pripper is freely available and can be downloaded from http://users.utu.fi/mijopi/Pripper. CONCLUSIONS: We have developed Pripper, a tool for reading an arbitrary number of proteins in FASTA format, predicting their caspase cleavage sites and outputting the cleaved sequences to a new FASTA format sequence file. We show that Pripper is a valuable tool in identifying novel caspase target proteins from modern proteomics experiments.

Compid: a new software tool to integrate and compare MS/MS based protein identification results from Mascot and Paragon.

Tandem mass spectrometry-based proteomics experiments produce large amounts of raw data, and different database search engines are needed to reliably identify all the proteins from this data. Here, we present Compid, an easy-to-use software tool that can be used to integrate and compare protein identification results from two search engines, Mascot and Paragon. Additionally, Compid enables extraction of information from large Mascot result files that cannot be opened via the Web interface and calculation of general statistical information about peptide and protein identifications in a data set. To demonstrate the usefulness of this tool, we used Compid to compare Mascot and Paragon database search results for mitochondrial proteome sample of human keratinocytes. The reports generated by Compid can be exported and opened as Excel documents or as text files using configurable delimiters, allowing the analysis and further processing of Compid output with a multitude of programs. Compid is freely available and can be downloaded from http://users.utu.fi/lanatr/compid. It is released under an open source license (GPL), enabling modification of the source code. Its modular architecture allows for creation of supplementary software components e.g. to enable support for additional input formats and report categories.

Mahalanobis distance screening of Arabidopsis mutants with chlorophyll fluorescence.

Rapid nondestructive screening of mutants is a common step in many research projects in plant biology. Here we report the development of a method that uses kinetic imaging of chlorophyll fluorescence to detect phenotypes that differ from wild-type plants. The method uses multiple fluorescence features simultaneously in order to catch different types of photosynthesis-related mutants with a single assay. The Mahalanobis distance was used to evaluate the degree of similarity in fluorescence features between the wild-type and test plants, and plants differing strongly from the wild-type were classified as mutants. The method was tested on a collection of photosynthesis-related mutants of Arabidopsis thaliana. The plants were evaluated from images in which the color of each pixel depended on the Mahalanobis distance of the fluorescence features. Two parameters of the color-coding procedure were used to adjust the trade-off between detection of true mutants and erratic classification of wild-type plants as mutants. We found that a large percentage of photosynthesis-related mutants can be detected with this method. Scripts for the free statistics software R are provided to facilitate the practical application of the method.

Filtering strategies for improving protein identification in high-throughput MS/MS studies.

Despite the recent advances in streamlining high-throughput proteomic pipelines using tandem mass spectrometry (MS/MS), reliable identification of peptides and proteins on a larger scale has remained a challenging task, still involving a considerable degree of user interaction. Recently, a number of papers have proposed computational strategies both for distinguishing poor MS/MS spectra prior to database search (pre-filtering) as well as for verifying the peptide identifications made by the search programs (post-filtering). Both of these filtering approaches can be very beneficial to the overall protein identification pipeline, since they can remove a substantial part of the time consuming manual validation work and convert large sets of MS/MS spectra into more reliable and interpretable proteome information. The choice of the filtering method depends both on the properties of the data and on the goals of the experiment. This review discusses the different pre- and post-filtering strategies available to the researchers, together with their relative merits and potential pitfalls. We also highlight some additional research topics, such as spectral denoising and statistical assessment of the identification results, which aim at further improving the coverage and accuracy of high-throughput protein identification studies.

Missing value imputation improves clustering and interpretation of gene expression microarray data.

BACKGROUND: Missing values frequently pose problems in gene expression microarray experiments as they can hinder downstream analysis of the datasets. While several missing value imputation approaches are available to the microarray users and new ones are constantly being developed, there is no general consensus on how to choose between the different methods since their performance seems to vary drastically depending on the dataset being used. RESULTS: We show that this discrepancy can mostly be attributed to the way in which imputation methods have traditionally been developed and evaluated. By comparing a number of advanced imputation methods on recent microarray datasets, we show that even when there are marked differences in the measurement-level imputation accuracies across the datasets, these differences become negligible when the methods are evaluated in terms of how well they can reproduce the original gene clusters or their biological interpretations. Regardless of the evaluation approach, however, imputation always gave better results than ignoring missing data points or replacing them with zeros or average values, emphasizing the continued importance of using more advanced imputation methods. CONCLUSION: The results demonstrate that, while missing values are still severely complicating microarray data analysis, their impact on the discovery of biologically meaningful gene groups can - up to a certain degree - be reduced by using readily available and relatively fast imputation methods, such as the Bayesian Principal Components Algorithm (BPCA).