Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context

Generation of a High Number of Healthy Erythroid Cells from Gene-Edited Pyruvate Kinase Deficiency Patient-Specific Induced Pluripotent Stem Cells

Pyruvate kinase deficiency (PKD) is a rare erythroid metabolic disease caused by mutations in the PKLR gene. Erythrocytes from PKD patients show an energetic imbalance causing chronic non-spherocytic hemolytic anemia, as pyruvate kinase defects impair ATP production in erythrocytes. We generated PKD induced pluripotent stem cells (PKDiPSCs) from peripheral blood mononuclear cells (PB-MNCs) of PKD patients by non-integrative Sendai viral vectors. PKDiPSCs were gene edited to integrate a partial codon-optimized R-type pyruvate kinase cDNA in the second intron of the PKLR gene by TALEN-mediated homologous recombination (HR). Notably, we found allele specificity of HR led by the presence of a single-nucleotide polymorphism. High numbers of erythroid cells derived from gene-edited PKDiPSCs showed correction of the energetic imbalance, providing an approach to correct metabolic erythroid diseases and demonstrating the practicality of this approach to generate the large cell numbers required for comprehensive biochemical and metabolic erythroid analyses.info:eu-repo/semantics/publishedVersio

From father to son: transgenerational effect of tetracycline on sperm viability

The broad-spectrum antibiotic tetracycline is used in animal production, antimicrobial therapy, and for curing arthropods infected with bacterial endosymbionts such as Wolbachia. Tetracycline inhibits mitochondrial translation, and recent evidence indicates that male reproductive traits may be particularly sensitive to this antibiotic. Here, we report the first multi-generation investigation of tetracycline's effects on ejaculate traits. In a study of the pseudoscorpion, Cordylochernes scorpioides, in which siblings were randomly assigned to control and tetracycline treatments across replicate full-sibling families, tetracycline did not affect body size in either sex, female reproduction or sperm number. However, tetracycline-treated males exhibited significantly reduced sperm viability compared to control males, and transmitted this toxic effect of tetracycline on sperm to their untreated sons but not to their F2 grandsons. These results are consistent with tetracycline-induced epigenetic changes in the male germline, and suggest the need for further investigation of transgenerational effects of tetracycline on male reproductive function

Performance of two questionnaires to measure treatment adherence in patients with Type-2 Diabetes

<p>Abstract</p> <p>Background</p> <p>Most valid methods to measure treatment adherence require time and resources, and they are not easily applied in highly demanding Primary Health Care Clinics (PHCC). The objective of this study was to determine sensitivity, specificity, predictive values, likelihood ratios, and post-test probabilities of two novel questionnaires as proxy measurements of treatment adherence in Type-2 diabetic patients.</p> <p>Methods</p> <p>Two questionnaires were developed by a group of experts to identify the patient's medical prescription knowledge (knowledge) and their attitudes toward treatment adherence (attitudes) as proxy measurements of adherence. The questionnaires were completed by patients receiving care in PHCC pertaining to the Mexican Institute of Social Security in Aguascalientes (Mexico). Pill count was used as gold standard. Participants were selected randomly, and their oral hypoglycemic prescriptions were studied. The main outcome measures for each questionnaire were sensitivity, specificity, predictive values, likelihood ratios, and post-test probabilities, all as an independent questionnaire test and in a serial analysis.</p> <p>Results</p> <p>Adherence prevalence was 27.0% using pill count. Knowledge questionnaire showed the highest sensitivity (68.1%) and negative predictive value (82.2%), the lowest negative likelihood ratio (0.58) and post-test probability for a negative result (0.16). Serial analysis showed the highest specificity (77.4%) and positive predictive value (40.1%) as well as the highest positive likelihood ratio (1.8) and post-test probability for a positive result (0.39).</p> <p>Conclusion</p> <p>Medical Prescription Knowledge questionnaire showed the best performance as proxy measurement to identify non-adherence in type 2 diabetic patients regarding negative predictive value, negative likelihood ratio, and post-test probability for a negative result. However, Medical Prescription Knowledge questionnaire performance may change in contexts with higher adherence prevalence. Therefore, more research is needed before using this method in other contexts.</p

Proteomic Analysis of the Cyst Stage of Entamoeba histolytica

We used tandem mass spectrometry to identify E. histolytica cyst proteins in 5 cyst positive stool samples. We report the identification of 417 non-redundant E. histolytica proteins including 195 proteins that were not identified in existing trophozoite derived proteome or EST datasets, consistent with cyst specificity. Because the cysts were derived directly from patient samples with incomplete purification, a limited number of proteins were identified (N = 417) that probably represent only a partial proteome. Nevertheless, the study succeeded in identifying proteins that are likely to be abundant in the cyst stage of the parasite. Several of these proteins may play roles in E. histolytica stage conversion or cyst function. Proteins identified in this study may be useful markers for diagnostic detection of E. histolytica cysts. Overall, the data generated in this study promises to aid the understanding of the cyst stage of the parasite which is vital for disease transmission and pathogenesis in E. histolytica

Bayesian Approach to Model CD137 Signaling in Human M.tuberculosis in vitro Responses

Abstract Immune responses are qualitatively and quantitatively influenced by a complex network of receptor-ligand interactions. Among them, the CD137:CD137L pathway is known to modulate innate and adaptive human responses against Mycobacterium tuberculosis. However, the underlying mechanisms of this regulation remain unclear. In this work, we developed a Bayesian Computational Model (BCM) of in vitro CD137 signaling, devised to fit previously gathered experimental data. The BCM is fed with the data and the prior distribution of the model parameters and it returns theirposterior distribution and the model evidence, which allows comparing alternative signaling mechanisms. The BCM uses a coupled system of non-linear differential equations to describe the dynamics of Antigen Presenting Cells, Natural Killer and T Cells together with the interpheron (IFN)-c and tumor necrosis factor (TNF)-a levels in the media culture. Fast and complete mixing of the media is assumed. The prior distribution of the parameters that describe the dynamics of the immunological response was obtained from the literature and theoretical considerations Our BCM applies successively the Levenberg-Marquardt algorithm to find the maximum a posteriori likelihood (MAP); the Metropolis Markov Chain Monte Carlo method to approximate the posterior distribution of the parameters and Thermodynamic Integration to calculate the evidence of alternative hypothesis. Bayes factors provided decisive evidence favoring direct CD137 signaling on T cells. Moreover, the posterior distribution of the parameters that describe the CD137 signaling showed that the regulation of IFNc levels is based more on T cells survival than on direct induction. Furthermore, the mechanisms that account for the effect of CD137 signaling on TNF-a production were based on a decrease of TNF-a production by APC and, perhaps, on the increase in APC apoptosis. BCM proved to be a useful tool to gain insight on the mechanisms of CD137 signaling during human response against Mycobacterium tuberculosis.Fil: Darío A Fernández Do Porto. UNIV.DE BUENOS AIRES. FAC.DE CS.EXACTAS Y NATURALES. UNIV.DE BUENOS AIRES. FAC.DE CS.EXACTAS Y NATURALES. INST QUIM FISICA D/L/MATERIALES MED AMB Y ENERG.Fil: Jerónimo Auzmendi. UNIV.DE BUENOS AIRES. FAC.DE CS.EXACTAS Y NATURALES. INST QUIM FISICA D/L/MATERIALES MED AMB Y ENERG.Fil: Delfina Peña. UNIV.DE BUENOS AIRES. FAC.DE CS.EXACTAS Y NATURALES. CONSEJO NAC.DE INVEST.CIENTIF.Y TECNICAS. OFICINA DE COORDINACION ADMINISTRATIVA CIUDAD UNIVERSITARIA. INSTITUTO DE QUIMICA BIOLOGICA DE LA FACULTAD DE CS. EXACTAS Y NATURALES. UNIV.DE BUENOS AIRES. FAC.DE CS.EXACTAS Y NATURALES. DTO.DE QUIMICA BIOLOGICA.Fil: Veronica E Garcia. CONSEJO NAC.DE INVEST.CIENTIF.Y TECNICAS. OFICINA DE COORDINACION ADMINISTRATIVA CIUDAD UNIVERSITARIA. INSTITUTO DE QUIMICA BIOLOGICA DE LA FACULTAD DE CS. EXACTAS Y NATURALES.Fil: Luciano Moffatt. UNIV.DE BUENOS AIRES. FAC.DE CS.EXACTAS Y NATURALES. INST QUIM FISICA D/L/MATERIALES MED AMB Y ENERG

Optimization of Control Strategies for Non-Domiciliated Triatoma dimidiata, Chagas Disease Vector in the Yucatán Peninsula, Mexico

Chagas disease is the most important vector-borne disease in Latin America. Residual insecticide spraying has been used successfully for the elimination of domestic vectors in many regions. However, some vectors of non-domestic origin are able to invade houses, and they are now a key challenge for further disease control. We developed a mathematical model to predict the temporal variations in abundance of non-domiciliated vectors inside houses, based on triatomine demographic parameters. The reliability of the predictions was demonstrated by comparing these with different sets of insect collection data from the Yucatan peninsula, Mexico. We then simulated vector control strategies based on insecticide spraying, insect, screens and bednets to evaluate their efficacy at reducing triatomine abundance in the houses. An optimum reduction in bug abundance by at least 80% could be obtained by insecticide application only when doses of at least 50 mg/m2 were applied every year within a two-month period matching the house invasion season by bugs. Alternatively, the use of insect screens consistently reduced bug abundance in the houses and offers a sustainable alternative. Such screens may be part of novel interventions for the integrated control of various vector-borne diseases