Digital versatile discs as platforms for multiplexed genotyping based on selective ligation and universal microarray detection

[EN] The development of a high-performance assay readout using integrated detectors is a current challenge in the implementation of DNA tests in diagnostic laboratories, particularly for supporting pharmacogenetic tests. A method for allelic discrimination, associated with single nucleotide polymorphisms (SNPs), is presented. Genomic DNA is extracted from blood and buccal swab samples. The procedure comprises fast multiplex ligation-dependent probe amplification, PCR amplification using universal primers and subsequent barcode hybridization. In this last step, each product is recognized by the specific probes immobilized on the surface of an optical disc. Assay results can be obtained with a disc reader. The optical sensing method in a DNA microarray format was optimized and evaluated for the simultaneous identification of 28 polymorphisms associated with psychiatric pharmacogenomics. The target biomarkers were located in the genes related to drug-metabolizing enzymes and drug transporters. The multiplexing capability and assay selectivity strongly depended on correct design (ligation probes, tails and barcodes). The discriminant analysis of reader outputs (spot intensities) led to patients being classified into different allelic populations. The obtained assignations correlated properly with the results provided by the reference technique (bead arrays), and the assay ended in an 8-fold shorter time using affordable equipment. The combination of a highly selective genotyping reaction as array-MLPA and the compact disc technology provides a reliable point-of-care approach. This genotyping tool is useful for the selection of personalized drug therapies in decentralized clinical laboratories.The authors acknowledge the financial support received from the Generalitat Valenciana (GVA-PROMETEOII/2014/040 Project) through the FEDER funds and the Spanish Ministry of Economy and Competitiveness (the MINECO CTQ2016-75749- R Project)Tortajada-Genaro, LA.; Niñoles Rodenes, R.; Mena-Mollá, S.; Maquieira Catala, A. (2019). Digital versatile discs as platforms for multiplexed genotyping based on selective ligation and universal microarray detection. The Analyst. 144:707-715. https://doi.org/10.1039/C8AN01830HS70771514

Effects of a single session of SMR neurofeedback training on anxiety and cortisol levels.

Objectives. According to some studies, a putatively calming effect of EEG neurofeedback training could be useful as a therapeutic tool in psychiatric practice. With the aim of elucidating this possibility, we tested the efficacy of a single session of ↑sensorimotor (SMR)/↓theta neurofeedback training for mood improvement in 32 healthy men, taking into account trainability, independence and interpretability of the results. Methods. A pre-post design, with the following dependent variables, was applied: (i) psychometric measures of mood with regards to anxiety, depression, and anger (Profile of Mood State, POMS, and State Trait Anxiety Inventory, STAI); (ii) biological measures (salivary levels of cortisol); (iii) neurophysiological measures (EEG frequency band power analysis). In accordance with general recommendations for research in neurofeedback, a control group receiving sham neurofeedback was included. Results. Anxiety levels decreased after the real neurofeedback and increased after the sham neurofeedback (P < 0.01, size effect 0.9 for comparison between groups). Cortisol decreased after the experiment in both groups, though with significantly more pronounced effects in the desired direction after the real neurofeedback (P < 0.04; size effect 0.7). The group receiving real neurofeedback significantly enhanced their SMR band (P < 0.004; size effect 0.88), without changes in the theta band. The group receiving sham neurofeedback did not show any EEG changes

A portable geometry-independent tomographic system for gamma-ray, a next generation of nuclear waste characterization

One of the main activities of the nuclear industry is the characterisation of radioactive waste based on the detection of gamma radiation. Large volumes of radioactive waste are classified according to their average activity, but often the radioactivity exceeds the maximum allowed by regulators in specific parts of the bulk. In addition, the detection of the radiation is currently based on static detection systems where the geometry of the bulk is fixed and well known. Furthermore, these systems are not portable and depend on the transport of waste to the places where the detection systems are located. However, there are situations where the geometry varies and where moving waste is complex. This is especially true in compromised situations.We present a new model for nuclear waste management based on a portable and geometry-independent tomographic system for three-dimensional image reconstruction for gamma radiation detection. The system relies on a combination of a gamma radiation camera and a visible camera that allows to visualise radioactivity using augmented reality and artificial computer vision techniques. This novel tomographic system has the potential to be a disruptive innovation in the nuclear industry for nuclear waste management

Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years

[EN] Objective To evaluate the effects of Process-Reengineering interventions on the Electronic Health Records (EHR) of a hospital over 7 years. Materials and methods Temporal Variability Assessment (TVA) based on probabilistic data quality assessment was applied to the historic monthly-batched admission data of Hospital La Fe Valencia, Spain from 2010 to 2016. Routine healthcare data with a complete EHR was expanded by processed variables such as the Charlson Comorbidity Index. Results Four Process-Reengineering interventions were detected by quantifiable effects on the EHR: (1) the hospital relocation in 2011 involved progressive reduction of admissions during the next four months, (2) the hospital services re-configuration incremented the number of inter-services transfers, (3) the care-services re-distribution led to transfers between facilities (4) the assignment to the hospital of a new area with 80,000 patients in 2015 inspired the discharge to home for follow up and the update of the pre-surgery planned admissions protocol that produced a significant decrease of the patient length of stay. Discussion TVA provides an indicator of the effect of process re-engineering interventions on healthcare practice. Evaluating the effect of facilities¿ relocation and increment of citizens (findings 1, 3¿4), the impact of strategies (findings 2¿3), and gradual changes in protocols (finding 4) may help on the hospital management by optimizing interventions based on their effect on EHRs or on data reuse. Conclusions The effects on hospitals EHR due to process re-engineering interventions can be evaluated using the TVA methodology. Being aware of conditioned variations in EHR is of the utmost importance for the reliable reuse of routine hospitalization data.F.J.P.B, C.S., J.M.G.G. and J.A.C. were funded Universitat Politecnica de Valencia, project "ANALISIS DE LA CALIDAD Y VARIABILIDAD DE DATOS MEDICOS". www.upv.es. J.M.G.G.is also partially supported by: Ministerio de Economia y Competitividad of Spain through MTS4up project (National Plan for Scientific and Technical Research and Innovation 2013-2016, No. DPI2016-80054-R); and European Commission projects H2020-SC1-2016-CNECT Project (No. 727560) and H2020-SC1-BHC-2018-2020 (No. 825750). The funders did not play any role in the study design, data collection and analysis, decision to publish, nor preparation of the manuscript.Perez-Benito, FJ.; Sáez Silvestre, C.; Conejero, JA.; Tortajada, S.; Valdivieso, B.; Garcia-Gomez, JM. (2019). Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years. PLoS ONE. 14(8):1-19. https://doi.org/10.1371/journal.pone.0220369S119148Aguilar-Savén, R. S. (2004). Business process modelling: Review and framework. International Journal of Production Economics, 90(2), 129-149. doi:10.1016/s0925-5273(03)00102-6Poulymenopoulou, M. (2003). Journal of Medical Systems, 27(4), 325-335. doi:10.1023/a:1023701219563Dadam P, Reichert M, Kuhn K. Clinical Workflows -The Killer Application for Process-oriented Information Systems? Proceedings of the 4th International Conference on Business Information Systems. London: Springer London; 2000. pp. 36–59. doi: https://doi.org/10.1007/978-1-4471-0761-3Lenz, R., & Reichert, M. (2007). IT support for healthcare processes – premises, challenges, perspectives. Data & Knowledge Engineering, 61(1), 39-58. doi:10.1016/j.datak.2006.04.007Rebuge, Á., & Ferreira, D. R. (2012). Business process analysis in healthcare environments: A methodology based on process mining. Information Systems, 37(2), 99-116. doi:10.1016/j.is.2011.01.003Amour EAEH, Ghannouchi SA. Applying Data Mining Techniques to Discover KPIs Relationships in Business Process Context. 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE; 2017. pp. 230–237. doi: https://doi.org/10.1109/PDCAT.2017.00045Chou, Y.-C., Chen, B.-Y., Tang, Y.-Y., Qiu, Z.-J., Wu, M.-F., Wang, S.-C., … Chuang, W.-C. (2010). Prescription-Filling Process Reengineering of an Outpatient Pharmacy. Journal of Medical Systems, 36(2), 893-902. doi:10.1007/s10916-010-9553-5Leu, J.-D., & Huang, Y.-T. (2009). An Application of Business Process Method to the Clinical Efficiency of Hospital. Journal of Medical Systems, 35(3), 409-421. doi:10.1007/s10916-009-9376-4Gand K. Investigating on Requirements for Business Model Representations: The Case of Information Technology in Healthcare. 2017 IEEE 19th Conference on Business Informatics (CBI). IEEE; 2017. pp. 471–480. doi: https://doi.org/10.1109/CBI.2017.36Ferreira, G. S. A., Silva, U. R., Costa, A. L., & Pádua, S. I. D. de D. (2018). The promotion of BPM and lean in the health sector: main results. Business Process Management Journal, 24(2), 400-424. doi:10.1108/bpmj-06-2016-0115Abdulrahman Jabour RM. Cancer Reporting: Timeliness Analysis and Process. 2016; Available: https://search.proquest.com/openview/4ecf737c5ef6d2d503e948df8031fe54/1?pq-origsite=gscholar&amp;cbl=18750&amp;diss=yHewitt M, Simone J V. Enhancing Data Systems to Improve the Quality of Cancer Care [Internet]. National Academy Press; 2000. Available: http://www.nap.edu/catalog/9970.htmlWeiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. doi:10.1136/amiajnl-2011-000681Saez C, Robles M, Garcia-Gomez JM. Comparative study of probability distribution distances to define a metric for the stability of multi-source biomedical research data. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. IEEE; 2013. pp. 3226–3229. doi: https://doi.org/10.1109/EMBC.2013.6610228Sáez, C., Rodrigues, P. P., Gama, J., Robles, M., & García-Gómez, J. M. (2014). Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Mining and Knowledge Discovery, 29(4), 950-975. doi:10.1007/s10618-014-0378-6Sáez, C., Zurriaga, O., Pérez-Panadés, J., Melchor, I., Robles, M., & García-Gómez, J. M. (2016). Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories. Journal of the American Medical Informatics Association, 23(6), 1085-1095. doi:10.1093/jamia/ocw010International Ethical Guidelines for Epidemiological Studies [Internet]. Geneva: Council for International Organizations of Medical Sciences (CIOMS) in collaboration with the World Health Organization; 2009. Available: https://cioms.ch/wp-content/uploads/2017/01/International_Ethical_Guidelines_LR.pdfResearch Ethics Committee of the Universitari i Politècnic La Fe Hospital [Internet]. Available: https://www.iislafe.es/en/research/ethics-committees/Charlson, M. E., Pompei, P., Ales, K. L., & MacKenzie, C. R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40(5), 373-383. doi:10.1016/0021-9681(87)90171-8Schneeweiss, S., Wang, P. S., Avorn, J., & Glynn, R. J. (2003). Improved Comorbidity Adjustment for Predicting Mortality in Medicare Populations. Health Services Research, 38(4), 1103-1120. doi:10.1111/1475-6773.00165Quan, H., Sundararajan, V., Halfon, P., Fong, A., Burnand, B., Luthi, J.-C., … Ghali, W. A. (2005). Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data. Medical Care, 43(11), 1130-1139. doi:10.1097/01.mlr.0000182534.19832.83Sáez Silvestre C. Probabilistic methods for multi-source and temporal biomedical data quality assessment [Internet]. Thesis. Universitat Politècnica de València. 2016. doi: https://doi.org/10.4995/Thesis/10251/62188Amari S, Nagaoka H. Methods of Information Geometry [Internet]. Amer. Math. Soc. and Oxford Univ. Press. American Mathematical Society; 2000. Available: https://books.google.es/books?hl=es&amp;lr=&amp;id=vc2FWSo7wLUC&amp;oi=fnd&amp;pg=PR7&amp;dq=Methods+of+Information+geometry&amp;ots=4HmyCCY4PX&amp;sig=2-dpCuwMQvEC1iREjxdfIX0yEls#v=onepage&amp;q=MethodsofInformationgeometry&amp;f=falseCsiszár, I., & Shields, P. C. (2004). Information Theory and Statistics: A Tutorial. Foundations and Trends™ in Communications and Information Theory, 1(4), 417-528. doi:10.1561/0100000004Lin, J. (1991). Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory, 37(1), 145-151. doi:10.1109/18.61115M.Cover T. Elements Of Information Theory Notes [Internet]. 2006. Available: http://books.google.fr/books?id=VWq5GG6ycxMC&printsec=frontcover&dq=intitle:Elements+of+Information+Theory&hl=&cd=1&source=gbs_api%5Cnpapers2://publication/uuid/BAF426F8-5A4F-44A4-8333-FA8187160D9BBrandes, U., & Pich, C. (s. f.). Eigensolver Methods for Progressive Multidimensional Scaling of Large Data. Lecture Notes in Computer Science, 42-53. doi:10.1007/978-3-540-70904-6_6Liaw, S. T., Rahimi, A., Ray, P., Taggart, J., Dennis, S., de Lusignan, S., … Talaei-Khoei, A. (2013). Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature. International Journal of Medical Informatics, 82(1), 10-24. doi:10.1016/j.ijmedinf.2012.10.001Arts, D. G. T. (2002). Defining and Improving Data Quality in Medical Registries: A Literature Review, Case Study, and Generic Framework. Journal of the American Medical Informatics Association, 9(6), 600-611. doi:10.1197/jamia.m1087Bray, F., & Parkin, D. M. (2009). Evaluation of data quality in the cancer registry: Principles and methods. Part I: Comparability, validity and timeliness. European Journal of Cancer, 45(5), 747-755. doi:10.1016/j.ejca.2008.11.032Parkin, D. M., & Bray, F. (2009). Evaluation of data quality in the cancer registry: Principles and methods Part II. Completeness. European Journal of Cancer, 45(5), 756-764. doi:10.1016/j.ejca.2008.11.033Fernandez-Llatas, C., Ibanez-Sanchez, G., Celda, A., Mandingorra, J., Aparici-Tortajada, L., Martinez-Millana, A., … Traver, V. (2019). Analyzing Medical Emergency Processes with Process Mining: The Stroke Case. Lecture Notes in Business Information Processing, 214-225. doi:10.1007/978-3-030-11641-5_17Fernandez-Llatas, C., Lizondo, A., Monton, E., Benedi, J.-M., & Traver, V. (2015). Process Mining Methodology for Health Process Tracking Using Real-Time Indoor Location Systems. Sensors, 15(12), 29821-29840. doi:10.3390/s151229769Van der Aalst, W., Weijters, T., & Maruster, L. (2004). Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering, 16(9), 1128-1142. doi:10.1109/tkde.2004.47Weijters AJMM, Van Der Aalst WMP, Alves De Medeiros AK. Process Mining with the HeuristicsMiner Algorithm [Internet]. Available: https://pdfs.semanticscholar.org/1cc3/d62e27365b8d7ed6ce93b41c193d0559d086.pdfShim, S. J., & Kumar, A. (2010). Simulation for emergency care process reengineering in hospitals. Business Process Management Journal, 16(5), 795-805. doi:10.1108/14637151011076476Svolba, G., & Bauer, P. (1999). Statistical Quality Control in Clinical Trials. Controlled Clinical Trials, 20(6), 519-530. doi:10.1016/s0197-2456(99)00029-xKahn, M. G., Raebel, M. A., Glanz, J. M., Riedlinger, K., & Steiner, J. F. (2012). A Pragmatic Framework for Single-site and Multisite Data Quality Assessment in Electronic Health Record-based Clinical Research. Medical Care, 50, S21-S29. doi:10.1097/mlr.0b013e318257dd67Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys, 41(3), 1-52. doi:10.1145/1541880.1541883Heinrich, B., Klier, M., & Kaiser, M. (2009). A Procedure to Develop Metrics for Currency and its Application in CRM. Journal of Data and Information Quality, 1(1), 1-28. doi:10.1145/1515693.1515697Sirgo, G., Esteban, F., Gómez, J., Moreno, G., Rodríguez, A., Blanch, L., … Bodí, M. (2018). Validation of the ICU-DaMa tool for automatically extracting variables for minimum dataset and quality indicators: The importance of data quality assessment. International Journal of Medical Informatics, 112, 166-172. doi:10.1016/j.ijmedinf.2018.02.007Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504-507. doi:10.1126/science.1127647Kohn LT, Corrigan JM. To err is human: building a safer health system. A report of the Committee on Quality of Health Care in America. 2000. p. 287. National Academies Press

Predicting morbidity by local similarities in multi-scale patient trajectories

[EN] Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.This work was supported by the CrowdHealth project (COLLECTIVE WISDOM DRIVING PUBLIC HEALTH POLICIES (727560)) and the MTS4up project (DPI2016-80054-R).Carrasco-Ribelles, LA.; Pardo-Más, JR.; Tortajada, S.; Sáez Silvestre, C.; Valdivieso, B.; Garcia-Gomez, JM. (2021). Predicting morbidity by local similarities in multi-scale patient trajectories. Journal of Biomedical Informatics. 120:1-9. https://doi.org/10.1016/j.jbi.2021.103837S1912

Genotyping of single nucleotide polymorphisms related to attention-deficit hyperactivity disorder

Pharmacological treatment of several diseases, such as attention-deficit hyperactivity disorder (ADHD), presents marked variability in efficiency and its adverse effects. The genotyping of specific single nucleotide polymorphisms (SNPs) can support the prediction of responses to drugs and the genetic risk of presenting comorbidities associated with ADHD. This study presents two rapid and affordable microarray-based strategies to discriminate three clinically important SNPs in genes ADRA2A, SL6CA2, and OPRM1 (rs1800544, rs5569, and rs1799971, respectively). These approaches are allele-specific oligonucleotide hybridization (ASO) and a combination of allele-specific amplification (ASA) and solid-phase hybridization. Buccal swab and blood samples taken from ADHD patients and controls were analyzed by ASO, ASA, and a gold-reference method. The results indicated that ASA is superior in genotyping capability and analytical performance.This research has been funded through projects FEDER MINECO INNPACTO IPT-2011-1132-010000, CTQ/2013/45875R, and PrometeoII/2014/040 (GVA).Tortajada-Genaro, LA.; Mena-Mollá, S.; Niñoles Rodenes, R.; Puigmule, M.; Viladevall, L.; Maquieira Catala, Á. (2016). Genotyping of single nucleotide polymorphisms related to attention-deficit hyperactivity disorder. Analytical and Bioanalytical Chemistry. 408(9):2339-2345. https://doi.org/10.1007/s00216-016-9332-3S233923454089Cortese S. The neurobiology and genetics of Attention-Deficit/Hyperactivity Disorder (ADHD): what every clinician should know. Eur J Paediatr Neurol. 2012;16:422–33.Contini V, Rovaris DL, Victor MM, Grevet EH, Rohde LA, Bau CH. Pharmacogenetics of response to methylphenidate in adult patients with attention-deficit/hyperactivity disorder (ADHD): a systematic review. Eur Neuropsychopharmacol. 2013;23:555–60.Gardiner SJ, Begg EJ. Pharmacogenetics, drug-metabolizing enzymes, and clinical practice. Pharmacol Rev. 2006;58(3):521–90.Abul-Husn NS, Obeng AO, Sanderson SC, Gottesman O, Scott SA. Implementation and utilization of genetic testing in personalized medicine. Pharmacogenomics Pers Med. 2014;7:227.Altman RB, Flockhart D, Goldstein DB, editors. Principles of pharmacogenetics and pharmacogenomics. Cambridge: Cambridge University Press; 2012.Hawi Z, Cummins TDR, Tong J, Johnson B, Lau R, Samarrai W, et al. The molecular genetic architecture of attention deficit hyperactivity disorder. Mol Psychiatry. 2015;20:289–97.Limaye N. Pharmacogenomics, Theranostics and Personalized Medicine-the complexities of clinical trials: challenges in the developing world. Appl Transl Genomics. 2013;2:17–21.Manolio TA, Chisholm RL, Ozenberger B, Roden DM, Williams MS, Wilson R, et al. Implementing genomic medicine in the clinic: the future is here. Genet Med. 2013;15:258–67.Kim S, Misra A. PharmGKB: the Pharmacogenomics Knowledge Base. Annu Rev Biomed Eng. 2007;9:289–320.Lucarelli F, Tombelli S, Minunni M, Marrazza G, Mascini M. Electrochemical and piezoelectric DNA biosensors for hybridisation detection. Anal Chim Acta. 2008;609:139–59.Knez K, Spasic D, Janssen KP, Lammertyn J. Emerging technologies for hybridization based single nucleotide polymorphism detection. Analyst. 2014;139:353–70.Choi JY, Kim YT, Byun JY, Ahn J, Chung S, Gweon DG, et al. Integrated allele-specific polymerase chain reaction–capillary electrophoresis microdevice for single nucleotide polymorphism genotyping. Lab Chip. 2012;12:5146–54.Ragoussis J. Genotyping Technologies for Genetic Research. Annu Rev Genomics Hum Genet. 2009;10:117–33.Sethi D, Gandhi RP, Kuma P, Gupta KC. Chemical strategies for immobilization of oligonucleotides. Biotechnol J. 2009;4:1513–29.Bañuls MJ, Morais SB, Tortajada-Genaro LA, Maquieira A. Microarray Developed on Plastic Substrates. Microarray Technology: Methods and Applications, 2016; 37-51.Tortajada-Genaro LA, Rodrigo A, Hevia E, Mena S, Niñoles R, Maquieira A. Microarray on digital versatile disc for identification and genotyping of Salmonella and Campylobacter in meat products. Anal Bioanal Chem. 2015;407:7285–94.Kieling C, Genro JP, Hutz MH, Rohde LA. A current update on ADHD pharmacogenomics. Pharmacogenomics. 2010;11:407–19.Kim BN, Kim JW, Cummins TD, Bellgrove MA, Hawi Z, Hong SB, et al. Norepinephrine genes predict response time variability and methylphenidate-induced changes in neuropsychological function in attention deficit hyperactivity disorder. J Clin Psychopharmacol. 2013;33:356–62.Carpentier PJ, Arias Vasquez A, Hoogman M, Onnink M, Kan CC, Kooij JJS, et al. Shared and unique genetic contributions to attention deficit/hyperactivity disorder and substance use disorders: A pilot study of six candidate genes. Eur Neuropsychopharmacol. 2013;23:448–57.Zhang Y, Haraksingh R, Grubert F, Abyzov A, Gerstein M, Weissman S, et al. Child development and structural variation in the human genome. Child Dev. 2013;84:34–48.Asari M, Watanabe S, Matsubara K, Shiono H, Shimizu K. Single nucleotide polymorphism genotyping by mini-primer allele-specific amplification with universal reporter primers for identification of degraded DNA. Anal Biochem. 2009;386:85–90.Choi JY, Kim YT, Ahn J, Kim KS, Gweon DG, Seo TS. Integrated allele-specific polymerase chain reaction–capillary electrophoresis microdevice for single nucleotide polymorphism genotyping. Biosens Bioelectron. 2012;35:327–34.Konstantou JK, Ioannou PC, Christopoulos TK. Dual-allele dipstick assay for genotyping single nucleotide polymorphisms by primer extension reaction. Eur J Hum Genet. 2009;17:105–11.Sebastian T, Cooney CG, Parker J, Qu P, Perov A, Golova JB, et al. Integrated amplification microarray system in a lateral flow cell for warfarin genotyping from saliva. Clin Chim Acta. 2014;429:198–205

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

[EN] Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovacion, Generalitat Valenciana.Albiol Colomer, A.; Albiol, F.; Paredes Palacios, R.; Plasencia-Martínez, JM.; Blanco Barrio, A.; García Santos, JM.; Tortajada, S.... (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights into Imaging. 13(1):1-12. https://doi.org/10.1186/s13244-022-01250-311213

Effect of providing citrus pulp‑integrated diet on fecal microbiota and serum and fecal metabolome shifts in crossbred pigs

The study aimed to assess the impact of dehydrated citrus pulp (DCP) on growth performance, fecal characteristics, fecal bacterial composition (based on 16S rRNA analysis), and fecal and serum metabolomic profiles in crossbred pigs. 80 finishing pigs Duroc × (Landrace × Large White) were fed either a control diet (C) or a diet with 240 g/kg DCP (T) for six weeks. Including DCP in diets tended to decrease feed intake, increased (p < 0.05) the concentrations of acetic and heptanoic acids and decreased (p < 0.05) fecal butyric and branched-chain fatty acid concentrations in feces. Animals fed DCP exhibited a lower abundance of the genera Clostridium and Romboutsia, while Lachnospira significantly increased. Orthogonal partial least squares discriminant analysis plotted a clear separation of fecal and serum metabolites between groups. The main discriminant fecal metabolites were associated with bacterial protein fermentation and were downregulated in T-fed pigs. In serum, DCP supplementation upregulated metabolites related to protein and fatty acids metabolism. In conclusion, the addition of DCP as an environmentally friendly source of nutrients in pig diets, resulted in modifications of fecal bacterial composition, fermentation patterns, and overall pig metabolism, suggesting improvements in protein metabolism and gut health

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.Project Chest screening for patients with COVID 19 (COV2000750 Special COVID19 resolution) funded by Instituto de Salud Carlos III. Project DIRAC (INNVA1/2020/42) funded by the Agencia Valenciana de la Innovación, Generalitat Valenciana.Peer reviewe