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.

Introduction: Malaria 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.Methods: In 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.Results: Comparative 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.Conclusion: The 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.

The gold standard diagnosis for malaria is the microscopic visualization of blood smears to identify Plasmodium parasites, although it is an expert-dependent technique and could trigger diagnostic errors. Artificial intelligence (AI) tools based on digital image analysis were postulated as a suitable supportive alternative for automated malaria diagnosis. A diagnostic evaluation of the iMAGING AI-based system was conducted in the reference laboratory of the International Health Unit Drassanes-Vall d’Hebron in Barcelona, Spain. iMAGING is an automated device for the diagnosis of malaria by using artificial intelligence image analysis tools and a robotized microscope. A total of 54 Giemsa-stained thick blood smear samples from travelers and migrants coming from endemic areas were employed and analyzed to determine the presence/absence of Plasmodium parasites. AI diagnostic results were compared with expert light microscopy gold standard method results. The AI system shows 81.25% sensitivity and 92.11% specificity when compared with the conventional light microscopy gold standard method. Overall, 48/54 (88.89%) samples were correctly identified [13/16 (81.25%) as positives and 35/38 (92.11%) as negatives]. The mean time of the AI system to determine a positive malaria diagnosis was 3 min and 48 s, with an average of 7.38 FoV analyzed per sample. Statistical analyses showed the Kappa Index = 0.721, demonstrating a satisfactory correlation between the gold standard diagnostic method and iMAGING results. The AI system demonstrated reliable results for malaria diagnosis in a reference laboratory in Barcelona. Validation in malaria-endemic regions will be the next step to evaluate its potential in resource-poor settings.

Gold nanoparticles (AuNPs) decorated with antigens have recently emerged as promising tools for vaccine development due to their innate ability to provide stability to antigens and modulate immune responses. In this study, we have engineered deactivated virus-like particles (VLPs) by precisely functionalizing gold cores with coronas comprising the full SARS-CoV-2 spike protein (S). Using BALB/c mice as a model, we investigated the immunogenicity of these S-AuNPs-VLPs. Our results demonstrate that S-AuNPs-VLPs consistently enhanced antigen-specific antibody responses compared to the S protein free in solution. This enhancement included higher binding antibody titers, higher neutralizing capacity of antibodies, and stronger T-cell responses. Compared to the mRNA COVID-19 vaccine, where the S protein is synthesized in situ, S-AuNPs-VLPs induced comparable binding and neutralizing antibody responses, but substantially superior T-cell responses. In conclusion, our study highlights the potential of conjugated AuNPs as an effective antigen-delivery system for protein-based vaccines targeting a broad spectrum of infectious diseases and other emergent viruses.

In this study, the HIV-1 P18I10 CTL peptide derived from the V3 loop of HIV-1 gp120 and the T20 anti-fusion peptide of HIV-1 gp41 were inserted into the HPV16 L1 capsid protein to construct chimeric HPV:HIV (L1:P18I10 and L1:T20) VLPs by using the mammalian cell expression system. The HPV:HIV VLPs were purified by chromatography. We demonstrated that the insertion of P18I10 or T20 peptides into the DE loop of HPV16 L1 capsid proteins did not affect in vitro stability, self-assembly and morphology of chimeric HPV:HIV VLPs. Importantly, it did not interfere either with the HIV-1 antibody reactivity targeting sequential and conformational P18I10 and T20 peptides presented on chimeric HPV:HIV VLPs or with the induction of HPV16 L1-specific antibodies in vivo. We observed that chimeric L1:P18I10/L1:T20 VLPs vaccines could induce HPV16- but weak HIV-1-specific antibody responses and elicited HPV16- and HIV-1-specific T-cell responses in BALB/c mice. Moreover, could be a potential booster to increase HIV-specific cellular responses in the heterologous immunization after priming with rBCG.HIVA vaccine. This research work would contribute a step towards the development of the novel chimeric HPV:HIV VLP-based vaccine platform for controlling HPV16 and HIV-1 infection, which is urgently needed in developing and industrialized countries.

Human papillomavirus (HPV) vaccines based on HPV L1 virus-like particles (VLPs) are already licensed but not accessible worldwide. About 38.0 million people were living with HIV in 2020 and there is no HIV vaccine yet. Therefore, safe, effective, and affordable vaccines against both viruses are an urgent need. In this study, the HIV-1 P18I10 CTL peptide from the V3 loop of HIV-1 gp120 glycoprotein was inserted into the HPV16 L1 protein to construct chimeric HPV:HIV (L1:P18I10) VLPs. Instead of the traditional baculovirus expression vector/insect cell (BEVS/IC) system, we established an alternative mammalian 293F cell-based expression system using cost-effective polyethylenimine-mediated transfection for L1:P18I10 protein production. Compared with conventional ultracentrifugation, we optimized a novel chromatographic purification method which could significantly increase L1:P18I10 VLP recovery (~56%). Chimeric L1:P18I10 VLPs purified from both methods were capable of self-assembling to integral particles and shared similar biophysical and morphological properties. After BALB/c mice immunization with 293F cell-derived and chromatography-purified L1:P18I10 VLPs, almost the same titer of anti-L1 IgG (p = 0.6409) was observed as Gardasil anti-HPV vaccine-immunized mice. Significant titers of anti-P18I10 binding antibodies (p < 0.01%) and P18I10-specific IFN-γ secreting splenocytes (p = 0.0002) were detected in L1:P18I10 VLP-immunized mice in comparison with licensed Gardasil-9 HPV vaccine. Furthermore, we demonstrated that insertion of HIV-1 P18I10 peptide into HPV16 L1 capsid protein did not affect the induction in anti-L1 antibodies. All in all, we expected that the mammalian cell expression system and chromatographic purification methods could be time-saving, cost-effective, scalable platforms to engineer bivalent VLP-based vaccines against HPV and HIV-1