Leishmania infantum and Dirofilaria immitis infections in Italy, 2009-2019: Changing distribution patterns

Background: For long time, canine leishmaniosis (CanL) was considered endemic in the southern, central, and insular regions of Italy, whereas heartworm disease (HW) caused by Dirofilaria immitis was considered endemic in the northern region and in the swampy Po Valley. Following the reports of new foci of both diseases, in this study we update the distribution patterns and occurrence of new foci of CanL and HW discussing the main drivers for the changes in the epidemiology of these two important zoonotic canine vector-borne diseases. Methods: Based on the statistical analyses of serological assays (n = 90,633) on L. infantum exposure and D. immitis infection performed by two reference diagnostic centres in Italy over a ten-year period (2009-2019) irrespective of the anamnesis of dogs. The distribution patterns of both parasites are herein presented along with the occurrence of new foci. Results: Results highlighted the changing distribution patterns of L. infantum vs D. immitis infection in Italy. CanL is endemic in some areas of northern regions and HW has endemic foci in central and southern regions and islands. Significant differences in L. infantum exposure and HW infection prevalence among the study macroareas were detected. The overall results of the positive tested samples were 28.2% in southern Italy and islands, 29.6% in central Italy and 21.6% in northern Italy for L. infantum and 2.83% in northern Italy, 7.75% in central Italy and 4.97% in southern Italy and islands for HW. HW positivity significantly varied over years (χ 2 = 108.401, df = 10, P < 0.0001), gradually increasing from 0.77% in 2009 to 8.47% in 2016-2017. Conclusions: New potential epidemiological scenarios are discussed according to a range of factors (e.g. environmental modifications, occurrence of competent insect vectors, transportation of infected animals to non-endemic areas, chemoprophylaxis or vector preventative measures), which may affect the current distribution. Overall, the results advocate for epidemiological surveillance programmes, more focussed preventative and control measures even in areas where few or no cases of both diseases have been diagnosed.[Figure not available: see fulltext.

Canine parvovirus (CPV) phylogeny is associated with disease severity

After its first identification in 1978, canine parvovirus (CPV) has been recognized all around the world as a major threat for canine population health. This ssDNA virus is characterized by a high substitution rate and several genetic and phenotypic variants emerged over time. Overall, the definition of 3 main antigenic variants was established based on specific amino acid markers located in a precise capsid position. However, the detection of several minor variants and incongruence observed between the antigenic classification and phylogeny have posed doubts on the reliability of this scheme. At the same time, CPV heterogeneity has favored the hypothesis of a differential virulence among variants, although no robust and consistent demonstration has been provided yet. The present study rejects the antigenic variant concept and attempts to evaluate the association between CPV strain phylogeny, reconstructed using the whole information contained in the VP2 coding gene, and several clinical and hemato-biochemical parameters, assessed from 34 CPV infected dogs at admission. By using different statistical approaches, the results of the present study show an association between viral phylogeny and host parameters ascribable to immune system, coagulation profile, acute phase response and, more generally, to the overall picture of the animal response. Particularly, a strong and significant phylogenetic signal was proven for neutrophil count and WBC. Therefore, despite the limited sample size, a relation between viral phylogeny and disease severity has been observed for the first time, suggesting that CPV virulence is an inherited trait. The likely existence of clades with different virulence highlights once more the relevance of intensive epidemiological monitoring and research on CPV evolution to better understand the virulence determinants, their epidemiology and develop adequate countermeasures

Seropositivity to canine tick-borne pathogens in a population of sick dogs in Italy

Background: Canine vector-borne diseases (CVBDs) associated to ticks are among the most important health issues affecting dogs. In Italy, Ehrlichia canis, Anaplasma spp., Rickettsia conorii and Borrelia burgdorferi (s.l.) have been studied in both healthy canine populations and those clinically ill with suspected CVBDs. However, little information is currently available on the overall prevalence and distribution of these pathogens in the country. The aim of this study was to assess the prevalence and distribution of tick-borne pathogens (TBPs) in clinically suspect dogs from three Italian macro areas during a 15-year period (2006–2020). Methods: A large dataset (n = 21,992) of serological test results for selected TBPs in three macro areas in Italy was analysed using a Chi-square test to evaluate the associations between the categorical factors (i.e. macro area, region, year, sex and age) and a standard logistic regression model (significance set at P = 0.05). Serological data were presented as annual and cumulative prevalence, and distribution maps of cumulative positive cases for TBPs were generated. Results: Of the tested serum samples, 86.9% originated from northern (43.9%) and central (43%) Italy. The majority of the tests was requested for the diagnosis of E. canis (47%; n = 10,334), followed by Rickettsia spp. (35.1%; n = 7725), B. burgdorferi (s.l.) (11.6%; n = 2560) and Anaplasma spp. (6.2%; n = 1373). The highest serological exposure was recorded for B. burgdorferi (s.l.) (83.5%), followed by Rickettsia spp. (64.9%), Anaplasma spp. (39.8%) and E. canis (28.7%). The highest number of cumulative cases of Borrelia burgdorferi (s.l.) was recorded in samples from Tuscany, central Italy. Rickettsia spp. was more prevalent in the south and on the islands, particularly in dogs on Sicily older than 6 years, whereas Anaplasma spp. was more prevalent in the north and E. canis more prevalent in the south and on the islands. Conclusions: The results of this study highlight the high seroprevalence and wide distribution of the four TBPs in dogs with clinically suspected CVBDs from the studied regions of Italy. The very high seroprevalence of B. burgdorferi (s.l.) exemplifies a limitation of this study, given the use of clinically suspect dogs and the possibility of cross-reactions when using serological tests. The present research provides updated and illustrative information on the seroprevalence and distribution of four key TBPs, and advocates for integrative control strategies for their prevention. Grapic abstract: [Figure not available: see fulltext.

Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale

Deriving disease subtypes from electronic health records (EHRs) can guide next-generation personalized medicine. However, challenges in summarizing and representing patient data prevent widespread practice of scalable EHR-based stratification analysis. Here we present an unsupervised framework based on deep learning to process heterogeneous EHRs and derive patient representations that can efficiently and effectively enable patient stratification at scale. We considered EHRs of 1,608,741 patients from a diverse hospital cohort comprising of a total of 57,464 clinical concepts. We introduce a representation learning model based on word embeddings, convolutional neural networks, and autoencoders (i.e., ConvAE) to transform patient trajectories into low-dimensional latent vectors. We evaluated these representations as broadly enabling patient stratification by applying hierarchical clustering to different multi-disease and disease-specific patient cohorts. ConvAE significantly outperformed several baselines in a clustering task to identify patients with different complex conditions, with 2.61 entropy and 0.31 purity average scores. When applied to stratify patients within a certain condition, ConvAE led to various clinically relevant subtypes for different disorders, including type 2 diabetes, Parkinson's disease and Alzheimer's disease, largely related to comorbidities, disease progression, and symptom severity. With these results, we demonstrate that ConvAE can generate patient representations that lead to clinically meaningful insights. This scalable framework can help better understand varying etiologies in heterogeneous sub-populations and unlock patterns for EHR-based research in the realm of personalized medicine.Comment: C.F. and R.M. share senior authorshi

Guida pratica alla trasfusione di sangue nel cane

La trasfusione di sangue è una procedura che consente il trasferimento di sangue (o com- ponenti o derivati) da un soggetto donatore sano a un soggetto ricevente. È considerata una sorta di trapianto e per questo è poten- zialmente soggetta a rischio di incompatibili- tà (cd. reazioni trasfusionali). In Italia la trasfusione di sangue intero nel cane è regolata dalla “Linea Guida relativa all’e- sercizio delle attività sanitarie riguardanti la medicina trasfusionale in campo veterinario” pubblicata sulla GU n. 32 del 7-2-2008, Suppl. Ord. N.32. La presente normativa, purtroppo, non contempla l’impiego di emocomponenti* i quali, a giudizio degli Autori, sono erronea- mente equiparati agli emoderivati** nella nor- mativa riguardante il farmaco veterinario§. L’obiettivo principale è quello di fornire al me- dico veterinario una guida di rapida consul- tazione per l’approccio pratico alla medicina trasfusionale del cane. Il progetto è stato rea- lizzato grazie alla collaborazione tra un gruppo di esperti di questo argomento (costituitosi come Gruppo di Studio Trasfusioni Veterinarie, GSTVet) e Bayer Healthcare-Animal Health che ha fornito il supporto per la pubblicazione di quanto elaborato dagli Autori. Le indicazioni ri- portate nella presente guida sono il frutto della consultazione della bibliografia scientifica in- ternazionale esistente in materia, non disgiunta dall’esperienza dei singoli Autori. Quanto di seguito riportato vuole rappresen- tare solo l’inizio di un percorso di aggiorna- mento più ampio che prevede di trattare nel prossimo futuro anche l’impiego degli emo- componenti e degli emoderivati, al momento non ancora disponibili in Italia per l’attività del medico veterinario. In questa prima parte sono illustrate la scelta e la gestione del cane donatore di sangue, le modalità della raccolta del sangue, la gestio- ne della sacca di sangue intero e le indicazio- ni terapeutiche della trasfusione di sangue intero, inclusa la gestione dell’emotrasfusio- ne. Questo articolato processo richiede la collaborazione di diverse figure professionali che devono: operare in sinergia al fine di tu- telare la salute e il benessere del donatore e del ricevente; offrire al ricevente il miglior in- tervento terapeutico possibile, fornendo un prodotto sicuro di elevata qualità sanitaria; ottimizzare l’utilizzo del sangue, prodotto di elevato valore biologico ed etico, di diffici- le reperibilità. Nel testo, volutamente sinte- tico per una sua più agevole consultazione, non sono stati riportati tutti i dettagli propri di una materia così complessa quale la trasfusione

Acute febrile illness is associated with Rickettsia spp infection in dogs

BACKGROUND: Rickettsia conorii is transmitted by Rhipicephalus sanguineus ticks and causes Mediterranean Spotted Fever (MSF) in humans. Although dogs are considered the natural host of the vector, the clinical and epidemiological significance of R. conorii infection in dogs remains unclear. The aim of this prospective study was to investigate whether Rickettsia infection causes febrile illness in dogs living in areas endemic for human MSF. METHODS: Dogs from southern Italy with acute fever (n = 99) were compared with case–control dogs with normal body temperatures (n = 72). Serology and real-time PCR were performed for Rickettsia spp., Ehrlichia canis, Anaplasma phagocytophilum/A. platys and Leishmania infantum. Conventional PCR was performed for Babesia spp. and Hepatozoon spp. Acute and convalescent antibodies to R. conorii, E. canis and A. phagocytophilum were determined. RESULTS: The seroprevalence rates at first visit for R. conorii, E. canis, A. phagocytophilum and L. infantum were 44.8%, 48.5%, 37.8% and 17.6%, respectively. The seroconversion rates for R. conorii, E. canis and A. phagocytophilum were 20.7%, 14.3% and 8.8%, respectively. The molecular positive rates at first visit for Rickettsia spp., E. canis, A. phagocytophilum, A. platys, L. infantum, Babesia spp. and Hepatozoon spp. were 1.8%, 4.1%, 0%, 2.3%, 11.1%, 2.3% and 0.6%, respectively. Positive PCR for E. canis (7%), Rickettsia spp. (3%), Babesia spp. (4.0%) and Hepatozoon spp. (1.0%) were found only in febrile dogs. The DNA sequences obtained from Rickettsia and Babesia PCRs positive samples were 100% identical to the R. conorii and Babesia vogeli sequences in GenBank®, respectively. Febrile illness was statistically associated with acute and convalescent positive R. conorii antibodies, seroconversion to R. conorii, E. canis positive PCR, and positivity to any tick pathogen PCRs. Fourteen febrile dogs (31.8%) were diagnosed with Rickettsia spp. infection based on seroconversion and/or PCR while only six afebrile dogs (12.5%) seroconverted (P = 0.0248). The most common clinical findings of dogs with Rickettsia infection diagnosed by seroconversion and/or PCR were fever, myalgia, lameness, elevation of C-reactive protein, thrombocytopenia and hypoalbuminemia. CONCLUSIONS: This study demonstrates acute febrile illness associated with Rickettsia infection in dogs living in endemic areas of human MSF based on seroconversion alone or in combination with PCR

A machine learning pipeline for quantitative phenotype prediction from genotype data

<p>Abstract</p> <p>Background</p> <p>Quantitative phenotypes emerge everywhere in systems biology and biomedicine due to a direct interest for quantitative traits, or to high individual variability that makes hard or impossible to classify samples into distinct categories, often the case with complex common diseases. Machine learning approaches to genotype-phenotype mapping may significantly improve Genome-Wide Association Studies (GWAS) results by explicitly focusing on predictivity and optimal feature selection in a multivariate setting. It is however essential that stringent and well documented Data Analysis Protocols (DAP) are used to control sources of variability and ensure reproducibility of results. We present a genome-to-phenotype pipeline of machine learning modules for quantitative phenotype prediction. The pipeline can be applied for the direct use of whole-genome information in functional studies. As a realistic example, the problem of fitting complex phenotypic traits in heterogeneous stock mice from single nucleotide polymorphims (SNPs) is here considered.</p> <p>Methods</p> <p>The core element in the pipeline is the L1L2 regularization method based on the naïve elastic net. The method gives at the same time a regression model and a dimensionality reduction procedure suitable for correlated features. Model and SNP markers are selected through a DAP originally developed in the MAQC-II collaborative initiative of the U.S. FDA for the identification of clinical biomarkers from microarray data. The L1L2 approach is compared with standard Support Vector Regression (SVR) and with Recursive Jump Monte Carlo Markov Chain (MCMC). Algebraic indicators of stability of partial lists are used for model selection; the final panel of markers is obtained by a procedure at the chromosome scale, termed ’saturation’, to recover SNPs in Linkage Disequilibrium with those selected.</p> <p>Results</p> <p>With respect to both MCMC and SVR, comparable accuracies are obtained by the L1L2 pipeline. Good agreement is also found between SNPs selected by the L1L2 algorithms and candidate loci previously identified by a standard GWAS. The combination of L1L2-based feature selection with a saturation procedure tackles the issue of neglecting highly correlated features that affects many feature selection algorithms.</p> <p>Conclusions</p> <p>The L1L2 pipeline has proven effective in terms of marker selection and prediction accuracy. This study indicates that machine learning techniques may support quantitative phenotype prediction, provided that adequate DAPs are employed to control bias in model selection.</p

Algebraic Comparison of Partial Lists in Bioinformatics

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset

Frequency of DEA 1 antigen in 1037 mongrel and PUREBREED dogs in Italy

Background: The prevalence of dog erythrocyte antigen (DEA 1) in canine population is approximately 40\u201360%. Often data are limited to a small number of breeds and/or dogs. The aims of this study were to evaluate frequency of DEA 1 in a large population of purebred and mongrel dogs including Italian native breeds and to recognize a possible association between DEA 1 and breed, sex, and genetic and phenotypical/functional classifications of breeds. Frequencies of DEA 1 blood group collected from screened/enrolled blood donors and from healthy and sick dogs were retrospectively evaluated. The breed and the sex were recorded when available. DEA 1 blood typing was assessed by immunocromatographic test on K3EDTA blood samples. The prevalence of DEA 1 antigen was statistically related to breed, gender, F\ue9d\ue9ration Cynologique Internationale (FCI) and genotypic grouping. Results: Sixty-two per cent dogs resulted DEA 1+ and 38% DEA 1-. DEA 1- was statistically associated with Dogo Argentino, Dobermann, German Shepherd, Boxer, Corso dogs, the molossian dogs, the FCI group 1, 2 and 3 and the genetic groups \u201cworking dogs\u201d and \u201cmastiff\u201d. DEA 1+ was statistically associated with Rottweiler, Briquet Griffon Vend\ue9en, Bernese mountain dog, Golden Retriever, the hunting breeds, the FCI group 4, 6, 7 and 8 and the genetic groups \u201cscent hounds\u201d and \u201cretrievers\u201d. No gender association was observed. Conclusions: Data obtained by this work may be clinically useful to drive blood donor enrollment and selection among different breeds

Effect of Size and Heterogeneity of Samples on Biomarker Discovery: Synthetic and Real Data Assessment

MOTIVATION: The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for the discovery of biomarkers using microarray data often provide results with limited overlap. These differences are imputable to 1) dataset size (few subjects with respect to the number of features); 2) heterogeneity of the disease; 3) heterogeneity of experimental protocols and computational pipelines employed in the analysis. In this paper, we focus on the first two issues and assess, both on simulated (through an in silico regulation network model) and real clinical datasets, the consistency of candidate biomarkers provided by a number of different methods. METHODS: We extensively simulated the effect of heterogeneity characteristic of complex diseases on different sets of microarray data. Heterogeneity was reproduced by simulating both intrinsic variability of the population and the alteration of regulatory mechanisms. Population variability was simulated by modeling evolution of a pool of subjects; then, a subset of them underwent alterations in regulatory mechanisms so as to mimic the disease state. RESULTS: The simulated data allowed us to outline advantages and drawbacks of different methods across multiple studies and varying number of samples and to evaluate precision of feature selection on a benchmark with known biomarkers. Although comparable classification accuracy was reached by different methods, the use of external cross-validation loops is helpful in finding features with a higher degree of precision and stability. Application to real data confirmed these results