Mapping layperson medical terminology into the Human Phenotype Ontology using neural machine translation models

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.eswa.2022.117446.In the medical domain there exists a terminological gap between patients and caregivers and the healthcare professionals. This gap may hinder the success of the communication between healthcare consumers and professionals in the field, with negative emotional and clinical consequences. In this work, we build a machine learning-based tool for the automatic translation between the terminology used by laypeople and that of the Human Phenotype Ontology (HPO). HPO is a structured vocabulary of phenotypic abnormalities found in human disease. Our method uses a vector space to represent an HPO-specific embedding as the output space for a neural network model trained on vector representations of layperson versions and other textual descriptors of medical terms. We explored different output embeddings coupled to different neural network architectures for the machine translation stage. We compute a similarity measure to evaluate the ability of the model to assign an HPO term to a layperson input. The best-performing models resulted with a similarity higher than 0.7 for more than 80% of the terms, with a median between 0.98 and 1. The translator model is made available in a web application at this link: https://hpotranslator.b2slab.upc.edu.This work was supported by the Spanish Ministry of Economy and Competitiveness (www.mineco.gob.es) TEC2014-60337-R, DPI2017-89827-R, Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), initiatives of Instituto de Investigación Carlos III (ISCIII), and Share4Rare project (Grant Agreement 780262). This work was partially funded by ACCIÓ (Innotec ACE014/20/000018). B2SLab is certified as 2017 SGR 952. The authors thank the NVIDIA Corporation for the donation of a Titan Xp GPU used to run the models presented in this article. J. Fonollosa acknowledges the support from the Serra Húnter program.Peer ReviewedPostprint (published version

Climate variability and change in the Euro-Mediterranean Region: results from a global AOGCM coupled with an interactive high-resolution model of the Mediterranean Sea

In this work we present and discuss the results obtained from a set of present and future climate simulations performed with a high-resolution model able to represent the dynamics of the Mediterranean Sea. The ability of the model to reproduce the basic features of the observed climate in the Mediterranean region and the beneficial effects of both atmospheric improved resolution and interactive Mediterranean Sea are assessed. In particular, the major characteristics of the variability in the Mediterranean basin and its connection with the large-scale circulation are investigated. Furthermore, the mechanisms through which global warming might affect the regional features of the climate are explored, focusing especially on the characteristics of the hydrological cycle

EFFECTS OF TROPICAL CYCLONES ON OCEAN HEAT TRANSPORT AS SIMULATED BY A HIGH RESOLUTION COUPLED GENERAL CIRCULATION MODEL

In this study the interplay between Tropical Cyclones (TCs) and the Northern hemispheric Ocean Heat Transport (OHT) is investigated. In particular, results from a numerical simulation of the 20th and 21st Century climate, following the Intergovernmental Panel for Climate Change (IPCC) 20C3M and A1B scenario protocols respectively have been analyzed. The numerical simulations have been performed using a state-of-the-art global atmosphere-ocean-sea-ice coupled general circulation model - CGCM (CMCC-MED, Gualdi et al. 2010, Scoccimarro et al. 2010) with relatively high-resolution (T159) in the atmosphere. The model is an evolution of the INGV-SXG (Gualdi et al. 2008, Bellucci et al. 2008) and the ECHAM-OPA-LIM (Fogli et al. 2009, Vichi et al. 2010) The simulated TCs exhibit realistic structure, geographical distribution (Fig.2) and interannual variability, indicating that the model is able to capture the basic mechanisms linking the TC activity with the large scale circulation. The cooling of the surface ocean observed in correspondence of the TCs is well simulated by the model (Fig.3). TC activity is shown to significantly affect the poleward OHT out of the tropics, and the heat transport into the deep tropics (Fig.4). This effect, investigated by looking at the 100 most intense Northern Hemisphere TCs, is strongly correlated with the TC-induced momentum flux at the ocean surface (Fig.7). TCs frequency and intensity appear to be substantially stationary through the whole 1950-2069 simulated period as well as the effect of the TCs on the meridional OHT

Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records

Altres ajuts: Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN); Instituto de Investigación Carlos III (ISCIII); CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM).Type 2 diabetes mellitus (T2DM) is a highly heterogeneous chronic disease with different pathophysiological and genetic characteristics affecting its progression, associated complications and response to therapies. The advances in deep learning (DL) techniques and the availability of a large amount of healthcare data allow us to investigate T2DM characteristics and evolution with a completely new approach, studying common disease trajectories rather than cross sectional values. We used an Kernelized-AutoEncoder algorithm to map 5 years of data of 11,028 subjects diagnosed with T2DM in a latent space that embedded similarities and differences between patients in terms of the evolution of the disease. Once we obtained the latent space, we used classical clustering algorithms to create longitudinal clusters representing different evolutions of the diabetic disease. Our unsupervised DL clustering algorithm suggested seven different longitudinal clusters. Different mean ages were observed among the clusters (ranging from 65.3±11.6 to 72.8±9.4). Subjects in clusters B (Hypercholesteraemic) and E (Hypertensive) had shorter diabetes duration (9.2±3.9 and 9.5±3.9 years respectively). Subjects in Cluster G (Metabolic) had the poorest glycaemic control (mean glycated hemoglobin 7.99±1.42%), while cluster E had the best one (mean glycated hemoglobin 7.04±1.11%). Obesity was observed mainly in clusters A (Neuropathic), C (Multiple Complications), F (Retinopathy) and G. A dashboard is available at dm2.b2slab.upc.edu to visualize the different trajectories corresponding to the 7 clusters

Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records

Diabetes tipus 2; Complicacions diabètiques; Registres de salut electrònicsDiabetes tipo 2; Complicaciones diabéticas; Registros de salud electrónicosType 2 diabetes; Diabetic complications; Electronic health recordsType 2 diabetes mellitus (T2DM) is a highly heterogeneous chronic disease with different pathophysiological and genetic characteristics affecting its progression, associated complications and response to therapies. The advances in deep learning (DL) techniques and the availability of a large amount of healthcare data allow us to investigate T2DM characteristics and evolution with a completely new approach, studying common disease trajectories rather than cross sectional values. We used an Kernelized-AutoEncoder algorithm to map 5 years of data of 11,028 subjects diagnosed with T2DM in a latent space that embedded similarities and differences between patients in terms of the evolution of the disease. Once we obtained the latent space, we used classical clustering algorithms to create longitudinal clusters representing different evolutions of the diabetic disease. Our unsupervised DL clustering algorithm suggested seven different longitudinal clusters. Different mean ages were observed among the clusters (ranging from 65.3±11.6 to 72.8±9.4). Subjects in clusters B (Hypercholesteraemic) and E (Hypertensive) had shorter diabetes duration (9.2±3.9 and 9.5±3.9 years respectively). Subjects in Cluster G (Metabolic) had the poorest glycaemic control (mean glycated hemoglobin 7.99±1.42%), while cluster E had the best one (mean glycated hemoglobin 7.04±1.11%). Obesity was observed mainly in clusters A (Neuropathic), C (Multiple Complications), F (Retinopathy) and G. A dashboard is available at dm2.b2slab.upc.edu to visualize the different trajectories corresponding to the 7 clusters

A random telegraph signal of Mittag-Leffler type

A general method is presented to explicitly compute autocovariance functions for non-Poisson dichotomous noise based on renewal theory. The method is specialized to a random telegraph signal of Mittag-Leffler type. Analytical predictions are compared to Monte Carlo simulations. Non-Poisson dichotomous noise is non-stationary and standard spectral methods fail to describe it properly as they assume stationarity.Comment: 13 pages, 3 figures, submitted to PR

Mechanistic and phenotypic studies of bicarinalin, BP100 and colistin action on Acinetobacter baumannii

Acinetobacter baumannii has been identified by the WHO as a high priority pathogen. It can be resistant to multiple antibiotics and colistin sulphate is often used as a last-resort treatment. However, the potentially severe side-effects of colistin are well documented and this study compared the bactericidal and anti-biofilm activity of two synthetic nature-inspired antimicrobial peptides, bicarinalin and BP100, with colistin. The minimum bactericidal concentration (MBC) against planktonic A. baumannii was approximately 0.5 μg/ml for colistin sulphate and ∼4 μg/ml for bicarinalin and BP100. A. baumannii commonly occurs as a biofilm and biofilm removal assay results highlighted that both bicarinalin and BP100 had significantly greater potential than colistin. Atomic force microscopy (AFM) showed dramatic changes in A. baumannii cell size and surface conformity when treated with peptide concentrations at and above the MBC. Scanning electron microscopy (SEM) visualised the reduction of biofilm coverage and cell surface changes as peptide concentration increased. Liposome assays revealed that these peptides most likely act as pore-forming agents in the membrane. Bicarinalin and BP100 may be effective therapeutic alternatives to colistin against A. baumannii infections but further research is required to assess if they elicit cytotoxicity issues in patients

Mutations in GDP-mannose pyrophosphorylase b cause congenital and limb-girdle muscular dystrophies associated with hypoglycosylation of α-dystroglycan

Congenital muscular dystrophies with hypoglycosylation of α-dystroglycan (α-DG) are a heterogeneous group of disorders often associated with brain and eye defects in addition to muscular dystrophy. Causative variants in 14 genes thought to be involved in the glycosylation of α-DG have been identified thus far. Allelic mutations in these genes might also cause milder limb-girdle muscular dystrophy phenotypes. Using a combination of exome and Sanger sequencing in eight unrelated individuals, we present evidence that mutations in guanosine diphosphate mannose (GDP-mannose) pyrophosphorylase B (GMPPB) can result in muscular dystrophy variants with hypoglycosylated α-DG. GMPPB catalyzes the formation of GDP-mannose from GTP and mannose-1-phosphate. GDP-mannose is required for O-mannosylation of proteins, including α-DG, and it is the substrate of cytosolic mannosyltransferases. We found reduced α-DG glycosylation in the muscle biopsies of affected individuals and in available fibroblasts. Overexpression of wild-type GMPPB in fibroblasts from an affected individual partially restored glycosylation of α-DG. Whereas wild-type GMPPB localized to the cytoplasm, five of the identified missense mutations caused formation of aggregates in the cytoplasm or near membrane protrusions. Additionally, knockdown of the GMPPB ortholog in zebrafish caused structural muscle defects with decreased motility, eye abnormalities, and reduced glycosylation of α-DG. Together, these data indicate that GMPPB mutations are responsible for congenital and limb-girdle muscular dystrophies with hypoglycosylation of α-DG. © 2013 The American Society of Human Genetics.Funding for UK10K was provided by the Wellcome Trust under award WT091310

Bowel preparation for elective colorectal resection: multi-treatment machine learning analysis on 6241 cases from a prospective Italian cohort

background current evidence concerning bowel preparation before elective colorectal surgery is still controversial. this study aimed to compare the incidence of anastomotic leakage (AL), surgical site infections (SSIs), and overall morbidity (any adverse event, OM) after elective colorectal surgery using four different types of bowel preparation. methods a prospective database gathered among 78 Italian surgical centers in two prospective studies, including 6241 patients who underwent elective colorectal resection with anastomosis for malignant or benign disease, was re-analyzed through a multi-treatment machine-learning model considering no bowel preparation (NBP; No. = 3742; 60.0%) as the reference treatment arm, compared to oral antibiotics alone (oA; No. = 406; 6.5%), mechanical bowel preparation alone (MBP; No. = 1486; 23.8%), or in combination with oAB (MoABP; No. = 607; 9.7%). twenty covariates related to biometric data, surgical procedures, perioperative management, and hospital/center data potentially affecting outcomes were included and balanced into the model. the primary endpoints were AL, SSIs, and OM. all the results were reported as odds ratio (OR) with 95% confidence intervals (95% CI). results compared to NBP, MBP showed significantly higher AL risk (OR 1.82; 95% CI 1.23-2.71; p = .003) and OM risk (OR 1.38; 95% CI 1.10-1.72; p = .005), no significant differences for all the endpoints were recorded in the oA group, whereas MoABP showed a significantly reduced SSI risk (OR 0.45; 95% CI 0.25-0.79; p = .008). conclusions MoABP significantly reduced the SSI risk after elective colorectal surgery, therefore representing a valid alternative to NBP

Abdominal drainage after elective colorectal surgery: propensity score-matched retrospective analysis of an Italian cohort

background: In italy, surgeons continue to drain the abdominal cavity in more than 50 per cent of patients after colorectal resection. the aim of this study was to evaluate the impact of abdominal drain placement on early adverse events in patients undergoing elective colorectal surgery. methods: a database was retrospectively analysed through a 1:1 propensity score-matching model including 21 covariates. the primary endpoint was the postoperative duration of stay, and the secondary endpoints were surgical site infections, infectious morbidity rate defined as surgical site infections plus pulmonary infections plus urinary infections, anastomotic leakage, overall morbidity rate, major morbidity rate, reoperation and mortality rates. the results of multiple logistic regression analyses were presented as odds ratios (OR) and 95 per cent c.i. results: a total of 6157 patients were analysed to produce two well-balanced groups of 1802 patients: group (A), no abdominal drain(s) and group (B), abdominal drain(s). group a versus group B showed a significantly lower risk of postoperative duration of stay >6 days (OR 0.60; 95 per cent c.i. 0.51-0.70; P < 0.001). a mean postoperative duration of stay difference of 0.86 days was detected between groups. no difference was recorded between the two groups for all the other endpoints. conclusion: this study confirms that placement of abdominal drain(s) after elective colorectal surgery is associated with a non-clinically significant longer (0.86 days) postoperative duration of stay but has no impact on any other secondary outcomes, confirming that abdominal drains should not be used routinely in colorectal surgery