Excess hospitalizations and mortality associated with seasonal influenza in Spain, 2008–2018

Hospitalization; Influenza; MortalityHospitalització; Grip; MortalitatHospitalización; Gripe; MortalidadBackground Influenza may trigger complications, particularly in at-risk groups, potentially leading to hospitalization or death. However, due to lack of routine testing, influenza cases are infrequently coded with influenza-specific diagnosis. Statistical models using influenza activity as an explanatory variable can be used to estimate annual hospitalizations and deaths associated with influenza. Our study aimed to estimate the clinical and economic burden of severe influenza in Spain, considering such models. Methods The study comprised ten epidemic seasons (2008/2009–2017/2018) and used two approaches: (i) a direct method of estimating the seasonal influenza hospitalization, based on the number of National Health Service hospitalizations with influenza-specific International Classification of Diseases (ICD) codes (ICD-9: 487–488; ICD-10: J09-J11), as primary or secondary diagnosis; (ii) an indirect method of estimating excess hospitalizations and deaths using broader groups of ICD codes in time-series models, computed for six age groups and four groups of diagnoses: pneumonia or influenza (ICD-9: 480–488, 517.1; ICD-10: J09–J18), respiratory (ICD-9: 460–519; ICD-10: J00–J99), respiratory or cardiovascular (C&R, ICD-9: 390–459, 460–519; ICD-10: I00–I99, J00–J99), and all-cause. Means, excluding the H1N1pdm09 pandemic (2009/2010), are reported in this study. Results The mean number of hospitalizations with a diagnosis of influenza per season was 13,063, corresponding to 28.1 cases per 100,000 people. The mean direct annual cost of these hospitalizations was €45.7 million, of which 65.7% was generated by patients with comorbidities. Mean annual influenza-associated C&R hospitalizations were estimated at 34,894 (min: 16,546; max: 52,861), corresponding to 75.0 cases per 100,000 (95% confidence interval [CI]: 63.3–86.3) for all ages and 335.3 (95% CI: 293.2–377.5) in patients aged ≥ 65 years. We estimate 3.8 influenza-associated excess C&R hospitalizations for each hospitalization coded with an influenza-specific diagnosis in patients aged ≥ 65 years. The mean direct annual cost of the estimated excess C&R hospitalizations was €142.9 million for all ages and €115.9 million for patients aged ≥ 65 years. Mean annual influenza-associated all-cause mortality per 100,000 people was estimated at 27.7 for all ages. Conclusions Results suggest a relevant under-detected burden of influenza mostly in the elderly population, but not neglectable in younger people.The BARI study was funded by Sanofi. Martinón-Torres F has received support for the present work from the Instituto de Salud Carlos III (Proyecto de Investigación en Salud, Acción Estratégica en Salud): Fondo de Investigación Sanitaria (FIS; PI070069/PI1000540/PI1601569/PI1901090) del plan nacional de I + D + I and ‘fondos FEDER’ and Proyectos GAIN Rescata-Covid_IN845D 2020/23 (GAIN, Xunta de Galicia)

Microorganisms as Shapers of Human Civilization, from Pandemics to Even Our Genomes: Villains or Friends? A Historical Approach

SARS-CoV-2; Influenza; MicrobiotaSARS-CoV-2; Influenza; MicrobiotaSARS-CoV-2; Influenza; MicrobiotaUniversal history is characterized by continuous evolution, in which civilizations are born and die. This evolution is associated with multiple factors, among which the role of microorganisms is often overlooked. Viruses and bacteria have written or decisively contributed to terrible episodes of history, such as the Black Death in 14th century Europe, the annihilation of pre-Columbian American civilizations, and pandemics such as the 1918 Spanish flu or the current COVID-19 pandemic caused by the coronavirus SARS-CoV-2. Nevertheless, it is clear that we could not live in a world without these tiny beings. Endogenous retroviruses have been key to our evolution and for the regulation of gene expression, and the gut microbiota helps us digest compounds that we could not otherwise process. In addition, we have used microorganisms to preserve or prepare food for millennia and more recently to obtain drugs such as antibiotics or to develop recombinant DNA technologies. Due to the enormous importance of microorganisms for our survival, they have significantly influenced the population genetics of different human groups. This paper will review the role of microorganisms as “villains” who have been responsible for tremendous mortality throughout history but also as “friends” who help us survive and evolve

Clinical evaluation of the Procleix SARS-CoV-2 assay, a sensitive, high-throughput test that runs on an automated system

COVID-19; Prueba de ácido nucleico; Amplificación mediada por transcripciónCOVID-19; Prova d'àcid nucleic; Amplificació mediada per la transcripcióCOVID-19; Nucleic acid test; Transcription-mediated amplificationTesting is crucial in controlling COVID-19. The Procleix® SARS-CoV-2 assay, a transcription-mediated amplification nucleic acid test that runs on an automated system, was evaluated using inactivated virus and clinical samples. The sensitivity of the assay was assessed using heat-inactivated SARS-CoV-2 and compared to 3 other tests. Clinical validation utilized 2 sets of samples: (1) Nasal, nasopharyngeal and oropharyngeal samples (n = 963) from asymptomatic individuals, and (2) nasopharyngeal samples from symptomatic patients: 100 positive and 100 negative by RT-PCR. The Procleix assay had greater sensitivity (3-fold to 100-fold) than the comparators and had high specificity (100%) in asymptomatic subjects. In symptomatic patients, the Procleix assay detected 100% of PCR-positives and found 24 positives in the initial PCR-negatives. Eighteen of these were confirmed positive and 6 were inconclusive. These studies showed that the Procleix SARS-CoV-2 assay was a sensitive and specific tool for detecting COVID-19.This work was sponsored by Grifols Diagnostic Solutions, Inc

Heterogeneous Infectivity and Pathogenesis of SARS-CoV-2 Variants Beta, Delta and Omicron in Transgenic K18-hACE2 and Wildtype Mice

SARS-CoV-2; Viral load; Wildtype miceSARS-CoV-2; Carga viral; Ratones de tipo salvajeSARS-CoV-2; Càrrega viral; Ratolins de tipus salvatgeThe emerging SARS-CoV-2 variants of concern (VOCs) may display enhanced transmissibility, more severity and/or immune evasion; however, the pathogenesis of these new VOCs in experimental SARS-CoV-2 models or the potential infection of other animal species is not completely understood. Here we infected K18-hACE2 transgenic mice with B.1, B.1.351/Beta, B.1.617.2/Delta and BA.1.1/Omicron isolates and demonstrated heterogeneous infectivity and pathogenesis. B.1.351/Beta variant was the most pathogenic, while BA.1.1/Omicron led to lower viral RNA in the absence of major visible clinical signs. In parallel, we infected wildtype (WT) mice and confirmed that, contrary to B.1 and B.1.617.2/Delta, B.1.351/Beta and BA.1.1/Omicron can infect them. Infection in WT mice coursed without major clinical signs and viral RNA was transient and undetectable in the lungs by day 7 post-infection. In silico modeling supported these findings by predicting B.1.351/Beta receptor binding domain (RBD) mutations result in an increased affinity for both human and murine ACE2 receptors, while BA.1/Omicron RBD mutations only show increased affinity for murine ACE2.The research of CBIG consortium (constituted by IRTA-CReSA, BSC & IrsiCaixa) is supported by Grifols. We thank Foundation Dormeur for financial support for the acquisition of the QuantStudio-5 real time PCR system. CÁ-N has a grant by Secretaria d’Universitats i Recerca de la Generalitat de Catalunya and Fons Social Europeu. EG-V is a research fellow from PERIS (SLT017/20/000090). This work was partially funded by grant PID2020-117145RB-I00 from the Spanish Ministry of Science and Innovation (NI-U) the Departament de Salut of the Generalitat de Catalunya (grant SLD016 to JB and Grant SLD015 to JC), the Spanish Health Institute Carlos III (Grant PI17/01518. PI20/00093 to JB and PI18/01332 to JC), Fundació La Marató de TV3 (Project202126-30-21), CERCA Programme/Generalitat de Catalunya 2017 SGR 252, and the crowdfunding initiatives #joemcorono (https://www.yomecorono.com), BonPreu/Esclat and Correos. Funded in part by Fundació Glòria Soler (JB). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

Deep learning; Malaria diagnosis; Microscopic examinationAprenentatge profund; Diagnòstic de malària; Examen microscòpicAprendizaje profundo; Diagnóstico de malaria; Examen microscópicoMalaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.The project is funded by the Microbiology Department of Vall d’Hebron Universitary Hospital, the Cooperation Centre of the Universitat Politècnica de Catalunya (CCD-UPC) and the Probitas Foundation

Erratum: Evaluation of a new, rapid, simple test for the detection of influenza virus

Following publication of [1] it has come to our attention that there was an error in the authorship order and in Jordi Vila's name, which should be Jordi Vila and not Jordi Vila Estape. We would like to sincerely apologize for the error and any inconvenience caused

The COVID-19 Sentinel Schools Network of Catalonia (CSSNC) project: Associated factors to prevalence and incidence of SARS-CoV-2 infection in educational settings during the 2020–2021 academic year

COVID-19; Medical risk factors; Virus testingCOVID-19; Factors de risc mèdics; Test de virusCOVID-19; Factores de riesgo médicos; Test de virusThe Sentinel Schools project was designed to monitor and evaluate the epidemiology of COVID-19 in Catalonia, gathering evidence for health and education policies to inform the development of health protocols and public health interventions to control of SARS-CoV-2 infection in schools. The aim of this study was to estimate the prevalence and incidence of SARS-CoV-2 infections and to identify their determinants among students and staff during February to June in the academic year 2020–2021. We performed two complementary studies, a cross-sectional and a longitudinal component, using a questionnaire to collect nominal data and testing for SARS-CoV-2 detection. We describe the results and perform a univariate and multivariate analysis. The initial crude seroprevalence was 14.8% (95% CI: 13.1–16.5) and 22% (95% CI: 18.3–25.8) for students and staff respectively, and the active infection prevalence was 0.7% (95% CI: 0.3–1) and 1.1% (95% CI: 0.1–2). The overall incidence for persons at risk was 2.73 per 100 person-month and 2.89 and 2.34 per 100 person-month for students and staff, respectively. Socioeconomic, self-reported knowledge, risk perceptions and contact pattern variables were positively associated with the outcome while sanitary measure compliance was negatively associated, the same significance trend was observed in multivariate analysis. In the longitudinal component, epidemiological close contact with SARS-CoV-2 infection was a risk factor for SARS-CoV-2 infection while the highest socioeconomic status level was protective as was compliance with sanitary measures. The small number of active cases detected in these schools suggests a low transmission among children in school and the efficacy of public health measures implemented, at least in the epidemiological scenario of the study period. The major contribution of this study was to provide results and evidence that help analyze the transmission dynamic of SARS-CoV-2 and evaluate the associations between sanitary protocols implemented, and measures to avoid SARS-CoV-2 spread in schools

iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope

IntroductionMalaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it.MethodsIn this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Plasmodium parasite detection. Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide.ResultsComparative analysis yielded a performance for YOLOv5x on a test set of 92.10% precision, 93.50% recall, 92.79% F-score, and 94.40% mAP0.5 for leukocyte, early and mature Plasmodium trophozoites overall detection. F-score values of each category were 99.0% for leukocytes, 88.6% for early trophozoites and 87.3% for mature trophozoites detection. Attention modules performance show non-significant statistical differences when compared to YOLOv5x original trained model. The predictive models were integrated into a smartphone-computer application for the purpose of image-based diagnostics in the laboratory. The system can perform a fully automated diagnosis by the auto-focus and X-Y movements of the robotized microscope, the CNN models trained for digital image analysis, and the smartphone device. The new prototype would determine whether a Giemsa-stained thick blood smear sample is positive/negative for Plasmodium infection and its parasite levels. The whole system was integrated into the iMAGING smartphone application.ConclusionThe coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases