COVID-19, caused by the SARS-CoV-2, poses significant global health challenges. A key player in its pathogenesis is the nucleocapsid protein (NP), which is crucial for viral replication and assembly. While NPs from other coronaviruses, such as SARS-CoV and MERS-CoV, are known to increase inflammation and cause acute lung injury, the specific effects of the SARS-CoV-2 NP on host cells remain largely unexplored. Recent findings suggest that the NP acts as a pathogen-associated molecular pattern (PAMP) that binds to Toll-like receptor 2 (TLR2), activating NF-κB (nuclear factor kappa-light-chain-enhancer of activated B cells) and MAPK (mitogen-activated protein kinase) signaling pathways. This activation is particularly pronounced in severe COVID-19 cases, leading to elevated levels of soluble ICAM-1 (intercellular adhesion molecule 1) and VCAM-1 (vascular cell adhesion molecule 1), which contribute to endothelial dysfunction and multiorgan damage. Furthermore, the NP is implicated in hyperinflammation and thrombosis—key factors in COVID-19 severity and long COVID. Its potential to bind with MASP-2 (mannan-binding lectin serine protease 2) may also be linked to persistent symptoms in long COVID patients. Understanding these mechanisms, particularly the role of the NP in thrombosis, is essential for developing targeted therapies to manage both acute and chronic effects of COVID-19 effectively. This comprehensive review aims to elucidate the multifaceted roles of the NP, highlighting its contributions to viral pathogenesis, immune evasion, and the exacerbation of thrombotic events, thereby providing insights into potential therapeutic targets for mitigating the severe and long-term impacts of COVID-19.

The complex link between COVID‐19 and immunometabolic diseases demonstrates the important interaction between metabolic dysfunction and immunological response during viral infections. Severe COVID‐19, defined by a hyperinflammatory state, is greatly impacted by underlying chronic illnesses aggravating the cytokine storm caused by increased levels of Pro‐inflammatory cytokines. Metabolic reprogramming, including increased glycolysis and altered mitochondrial function, promotes viral replication and stimulates inflammatory cytokine production, contributing to illness severity. Mitochondrial metabolism abnormalities, strongly linked to various systemic illnesses, worsen metabolic dysfunction during and after the pandemic, increasing cardiovascular consequences. Long COVID‐19, defined by chronic inflammation and immune dysregulation, poses continuous problems, highlighting the need for comprehensive therapy solutions that address both immunological and metabolic aspects. Understanding these relationships shows promise for effectively managing COVID‐19 and its long‐term repercussions, which is the focus of this review paper.

Introduction/Background: Cardiovascular symptoms appear in a high proportion of patients in the few months following a severe SARS-CoV-2 infection. Non-invasive methods to predict disease severity could help personalizing healthcare and reducing the occurrence of these symptoms. Research Questions/Hypothesis: We hypothesized that blood long noncoding RNAs (lncRNAs) and machine learning (ML) could help predict COVID-19 severity. Goals/Aims: To develop a model based on lncRNAs and ML for predicting COVID-19 severity. Methods/Approach: Expression data of 2906 lncRNAs were obtained by targeted sequencing in plasma samples collected at baseline from four independent cohorts, totaling 564 COVID-19 patients. Patients were aged 18+ and were recruited from 2020 to 2023 in the PrediCOVID cohort (n=162; Luxembourg), the COVID19_OMICS-COVIRNA cohort (n=100, Italy), the TOCOVID cohort (n=233, Spain), and the MiRCOVID cohort (n=69, Germany). The study complied with the Declaration of Helsinki. Cohorts were approved by ethics committees and patients signed an informed consent. Results/Data: After data curation and pre-processing, 463 complete datasets were included in further analysis, representing 101 severe patients (in-hospital death or ICU admission) and 362 stable patients (no hospital admission or hospital admission but not ICU). Feature selection with Boruta, a random forest-based method, identified age and five lncRNAs (LINC01088-201, FGDP-AS1, LINC01088-209, AKAP13, and a novel lncRNA) associated with disease severity, which were used to build predictive models using six ML algorithms. A naïve Bayes model based on age and five lncRNAs predicted disease severity with an AUC of 0.875 [0.868-0.881] and an accuracy of 0.783 [0.775-0.791]. Conclusion: We developed a ML model including age and five lncRNAs predicting COVID-19 severity. This model could help improve patients’ management and cardiovascular outcomes.

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82–0.84) and a balanced accuracy of 0.78 (95% CI 0.77–0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40–0.74). Quantitative PCR validated LEF1-AS1’s adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.

This research was focused on testing two water filters - Brita and Profissimo, which were filtering two and five liters of water every day. The lifespan of used filters is four weeks, while they have been actively used for eight weeks in this study to check for their efficiency after exceeded usage. Along with this, the quality of tap water, which was filtered using these two types of filters, was also tested. The experiment of the whole study was divided into three main stages: microbiological analysis, biochemical analysis, and UV-VIS spectrophotometric analysis of filtered water. The measurements were done every five days. The aim was to compare the performances of Brita and Profissimo filters after the completion of the required experiments. Based on the results that are obtained from all the analyses mentioned previously, we can conclude that Brita 2l filter was the most efficient, while Profissimo 5l filter appeared to be the least effective filter.  It is important to emphasize that the tap water in Sarajevo is generally clean and drinkable, so there is a possibility that when using more polluted water, greater deviations in the operation of filters can be observed. Overall, both water filters were usable even after two months of active usage and our measurements showed good water quality which lacks impurities and is safe for drinking.

Protein structure prediction is an important process that carries a lot of benefits for various areas of science and industry. Template modeling is the most reliable and most popular method, depending on the solved structures from the Protein Data Bank. An important part of it is template selection, using different methods, which is a challenging task that requires special attention because the proper selection of protein template can lead to a more accurate protein prediction. This study focuses on the relationships between predicted proteins, taken from the Swiss-model repository, and their templates, on a larger scale. Features of predicted proteins are taken into account, including protein length, sequence identity, and sequence coverage. Quality assessment scores are compared and analyzed between the predicted proteins and their templates. Overall, quality assessment scores of predicted proteins show a moderate positive correlation to the sequence identity with the templates. Moreover, based on our data, the level of template quality is noticeably correlated with the predicted protein structuers, because templates with higher quality scores will, on average, also allow for the modeling of predicted proteins with higher quality scores.

Proteins are in the focus of research due to their importance as biological catalysts in various cellular processes and diseases. Since the experimental study of proteins is time-consuming and expensive, in silico prediction and analysis of proteins is common. Template-based prediction is the most reliable, which is why the aim of this study is to analyze how important are the primary features of proteins for their quality score. Statistical analysis shows that protein models with a resolution lower than 3 A or R value lower than 0.25 have higher quality scores when compared individually to their counterparts. Machine learning algorithm random forest analysis also shows resolution to have the highest importance, while other features have lower but moderate importance scores. The exception is the presence of ligand in protein models, which does not have an effect on the global protein quality scores, both through statistical and machine learning analyses.