Purpose The aim was to describe the prevalence, molecular epidemiology and clinical manifestations of human bocavirus (HBoV) in patients attended at a tertiary hospital in Barcelona, Spain. Methods From October 2014 to May 2017, respiratory specimens from paediatric patients were collected for respiratory viruses’ laboratory-confirmation. Phylogenetic analyses from partial VP1 sequences were performed from all HBoV laboratory-confirmed specimens. Clinical features were retrospectively studied. Results 178/10271 cases were HBoV laboratory-confirmed. The median age was 1.53 (IQR 1.0–2.3). Co-detection was highly reported (136; 76%). All viruses belonged into HBoV1 genotype but one into HBoV2. Non-reported mutations were observed and two sites were suggestive to be under negative selection. 61% (109/178) cases had lower RTI (LRTI), of whom 84 had co-detections (77%) and 76 had comorbidities (70%). LRTI was the cause of hospitalization in 85 out of 109 cases (78%), and no differences were found regarding severity factors during hospitalization between co- and single-detections, except for median length of respiratory support, which was longer in cases with co-detections. Conclusions Close monitoring of predominant HBoV1 showed a high similarity between viruses. The presence of comorbidities might explain the high prevalence of LRTI. Symptomatology in HBoV single-detected cases suggest that HBoV is a true pathogen.

Malaria 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.