Visualization of mucosal field in HPV positive and negative oropharyngeal squamous cell carcinomas: combined genomic and radiology based 3D model

The aim of this study was to visualize the tumor propagation and surrounding mucosal field in radiography-based 3D model for advanced stage HNSCC and combine it with HPV genotyping and miRNA expression characterization of the visualized area. 25 patients with T1-3 clinical stage HNSCC were enrolled in mapping biopsy sampling. Biopsy samples were evaluated for HPV positivity and miR-21-5p, miR-143, miR-155, miR-221-5p expression in Digital Droplet PCR system. Significant miRNA expression differences of HPV positive tumor tissue biopsies were found for miR-21-5p, miR-143 and miR-221-5p compared to the HPV negative tumor biopsy series. Peritumoral mucosa showed patchy pattern alterations of miR-21-5p and miR-155 in HPV positive cases, while gradual change of miR-21-5p and miR-221-5p was seen in HPV negative tumors. In our study we found differences of the miRNA expression patterns among the HPV positive and negative tumorous tissues as well as the surrounding mucosal fields. The CT based 3D models of the cancer field and surrounding mucosal surface can be utilized to improve proper preoperative planning. Complex evaluation of HNSCC tissue organization field can elucidate the clinical and molecular differentiation of HPV positive and negative cases, and enhance effective organ saving therapeutic strategies

Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2

We present a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The method utilizes machine learning-guided image analysis and enables simultaneous measurement of immunoglobulin M (IgM), IgA, and IgG responses against different viral antigens in an automated and high-throughput manner. The assay relies on antigens expressed through transfection, enabling use at a low biosafety level and fast adaptation to emerging pathogens. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate that this method allows differentiation between vaccine-induced and infection-induced antibody responses. Additionally, we established a dedicated web page for quantitative visualization of sample-specific results and their distribution, comparing them with controls and other samples. Our results provide a proof of concept for the approach, demonstrating fast and accurate measurement of antibody responses in a research setup with prospects for clinical diagnostics

Bacterial Vaccinations in Patients with Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a frequent, often progressive, chronic disease of the lungs. Patients with COPD often have impaired immunity; therefore, they are prone to chest infections, such as pneumonia or bronchitis. Acute exacerbations of COPD are major events that accelerate disease progression, contributing to its symptoms’ burden, morbidity, and mortality. Both pneumonia and acute exacerbations in COPD are caused by bacteria against which there are effective vaccinations. Although the number of randomised controlled studies on bacterial vaccinations in COPD is limited, national and international guidelines endorse specific vaccinations in patients with COPD. This review will summarise the different types of vaccinations that prevent pneumonia and COPD exacerbations. We also discuss the results of early phase studies. We will mainly focus on Streptococcus pneumoniae, as this bacterium was predominantly investigated in COPD. However, we also review studies investigating vaccinations against Haemophilus influenzae, Moraxella catarrhalis, and Bordetella pertussis

Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2

Funding Information: The authors thank the Minerva Institute (Helsinki, Finland) for providing utilities for the project, Prof. Perttu Hämäläinen (Aalto University, Finland) for providing the expertise of his group for the project, the FIMM High Throughput Biomedicine Unit for providing access to high-throughput robotics, the FIMM High Content Imaging and Analysis Unit for HC imaging and analysis (HiLIFE, University of Helsinki and Biocenter Finland; EuroBioImaging, ISIDORe partner), and the CSC – IT Center for Science, Finland, for computational resources. We acknowledge support from the LENDULET-BIOMAG grant (2018-342), from the European Regional Development Funds ( GINOP-2.3.2-15-2016-00006 , GINOP-2.3.2-15-2016-00026 , and GINOP-2.3.2-15-2016-00037 ), from the H2020-discovAIR ( 874656 ), from the H2020 ATTRACT-SpheroidPicker , and from the Chan Zuckerberg Initiative , Seed Networks for the HCA-DVP. The Finnish TEKES/BusinessFinland FiDiPro Fellow Grant 40294/13 (to V.P., O.K., L.P., and P.H.), grants awarded by the Academy of Finland (iCOIN- 336496 to O.K., V.P., and O.V.; 308613 to J.H.; 321809 to T.S.; 310552 to L.P.; 337530 to I.J.; and FIRI2020-337036 to FIMM-HCA, A.H., L.P., V.P., and P.H.), the EU H2020 VEO project (O.V.), and a Minerva Foundation for COVID-19 Research project grant (to V.P.) are also acknowledged. C.G. is funded by the Academy of Finland Flagship program, Finnish Center for Artificial Intelligence. OrthoSera Ltd. was funded by NKFIH grants ( 2020-1.1.6-JÖVŐ-2021-00010 and TKP2020-NKA-17 ). The authors thank Dora Bokor, PharmD, for proofreading the manuscript.We present a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The method utilizes machine learning-guided image analysis and enables simultaneous measurement of immunoglobulin M (IgM), IgA, and IgG responses against different viral antigens in an automated and high-throughput manner. The assay relies on antigens expressed through transfection, enabling use at a low biosafety level and fast adaptation to emerging pathogens. Using severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the model pathogen, we demonstrate that this method allows differentiation between vaccine-induced and infection-induced antibody responses. Additionally, we established a dedicated web page for quantitative visualization of sample-specific results and their distribution, comparing them with controls and other samples. Our results provide a proof of concept for the approach, demonstrating fast and accurate measurement of antibody responses in a research setup with prospects for clinical diagnostics.Peer reviewe