EuCARE-POSTCOVID Study: a multicentre cohort study on long-term post-COVID-19 manifestations.

BACKGROUND: Post-COVID-19 condition refers to persistent or new onset symptoms occurring three months after acute COVID-19, which are unrelated to alternative diagnoses. Symptoms include fatigue, breathlessness, palpitations, pain, concentration difficulties ("brain fog"), sleep disorders, and anxiety/depression. The prevalence of post-COVID-19 condition ranges widely across studies, affecting 10-20% of patients and reaching 50-60% in certain cohorts, while the associated risk factors remain poorly understood. METHODS: This multicentre cohort study, both retrospective and prospective, aims to assess the incidence and risk factors of post-COVID-19 condition in a cohort of recovered patients. Secondary objectives include evaluating the association between circulating SARS-CoV-2 variants and the risk of post-COVID-19 condition, as well as assessing long-term residual organ damage (lung, heart, central nervous system, peripheral nervous system) in relation to patient characteristics and virology (variant and viral load during the acute phase). Participants will include hospitalised and outpatient COVID-19 patients diagnosed between 01/03/2020 and 01/02/2025 from 8 participating centres. A control group will consist of hospitalised patients with respiratory infections other than COVID-19 during the same period. Patients will be followed up at the post-COVID-19 clinic of each centre at 2-3, 6-9, and 12-15 months after clinical recovery. Routine blood exams will be conducted, and patients will complete questionnaires to assess persisting symptoms, fatigue, dyspnoea, quality of life, disability, anxiety and depression, and post-traumatic stress disorders. DISCUSSION: This study aims to understand post-COVID-19 syndrome's incidence and predictors by comparing pandemic waves, utilising retrospective and prospective data. Gender association, especially the potential higher prevalence in females, will be investigated. Symptom tracking via questionnaires and scales will monitor duration and evolution. Questionnaires will also collect data on vaccination, reinfections, and new health issues. Biological samples will enable future studies on post-COVID-19 sequelae mechanisms, including inflammation, immune dysregulation, and viral reservoirs. TRIAL REGISTRATION: This study has been registered with ClinicalTrials.gov under the identifier NCT05531773.

Bioinformatics analysis of the genes involved in the extension of prostate cancer to adjacent lymph nodes by supervised and unsupervised machine learning methods: The role of SPAG1 and PLEKHF2.

The present study aimed to identify the genes associated with the involvement of adjunct lymph nodes of patients with prostate cancer (PCa) and to provide valuable information for the identification of potential diagnostic biomarkers and pathological genes in PCa metastasis. The most important candidate genes were identified through several machine learning approaches including K-means clustering, neural network, Naïve Bayesian classifications and PCA with or without downsampling. In total, 21 genes associated with lymph nodes involvement were identified. Among them, nine genes have been identified in metastatic prostate cancer, six have been found in the other metastatic cancers and four in other local cancers. The amplification of the candidate genes was evaluated in the other PCa datasets. Besides, we identified a validated set of genes involved in the PCa metastasis. The amplification of SPAG1 and PLEKHF2 genes were associated with decreased survival in patients with PCa.

Dynamics of neural fields with exponential temporal kernel.

We consider the standard neural field equation with an exponential temporal kernel. We analyze the time-independent (static) and time-dependent (dynamic) bifurcations of the equilibrium solution and the emerging spatiotemporal wave patterns. We show that an exponential temporal kernel does not allow static bifurcations such as saddle-node, pitchfork, and in particular, static Turing bifurcations. However, the exponential temporal kernel possesses the important property that it takes into account the finite memory of past activities of neurons, which Green's function does not. Through a dynamic bifurcation analysis, we give explicit bifurcation conditions. Hopf bifurcations lead to temporally non-constant, but spatially constant solutions, but Turing-Hopf bifurcations generate spatially and temporally non-constant solutions, in particular, traveling waves. Bifurcation parameters are the coefficient of the exponential temporal kernel, the transmission speed of neural signals, the time delay rate of synapses, and the ratio of excitatory to inhibitory synaptic weights.

Assessing VOC emissions from different gas stations: impacts, variations, and modeling fluctuations of air pollutants.

Gas stations distributed around densely populated areas are responsible for toxic pollutant emissions such as volatile organic compounds (VOCs). This study aims to measure VOCs emission from three different kinds of gas stations to determine the extent of pollution from the gas stations and the most frequent type of VOC compound emitted. The concentrations of ambient VOCs at three refueling stations with a different type of fuels in Mashhad were monitored. The result of this study showed that CNG fuel stations are less polluting than petrol stations. In all the studied sites, the highest concentrations were related to xylene isomers, irrespective of the fuel type. Total VOCs at the supply of both compressed natural gas (CNG) and gasoline stations was 482.36 ± 563.45 µg m-3. At a CNG station and a gasoline station, total VOC concentrations were 1363.4 ± 1975 µg m-3 and 410.29 ± 483.37 µg m-3, respectively. The differences in concentrations of toluene and m,p-xylene between the fuel stations can be related to the quality and type of fuel, vapor recovery technology, fuel reserves, dripless nozzles, traffic density in these stations, meteorological conditions and the location of sampling sites. The combination of a sine function and a quadratic function could model the fluctuation behavior of air pollutants like m,p-xylene. In all the sites, the highest concentrations were related to xylene isomers, irrespective of the type of fuel. The changing rate of m,p-xylene pollutant in each station was also modeled in this study.

SARS-CoV-2 trends in Italy, Germany and Portugal and school opening during the period of Omicron variant dominance: A quasi experimental study in the EuCARE project.

OBJECTIVES: We investigated the impact of school reopening on SARS-CoV-2 transmission in Italy, Germany, and Portugal in autumn 2022 when the Omicron variant was prevalent. METHODS: A prospective international study was conducted using the case reproduction number (Rc) calculated with the time parametrization of Omicron. For Germany and Italy, staggered difference-in-differences analysis was employed to explore the causal relationship between school reopening and Rc changes, accounting for varying reopening dates. In Portugal, interrupted time series analysis was used due to simultaneous school reopenings. Multivariable models were adopted to adjust for confounders. RESULTS: In Italy and Germany, post-reopening Rc estimates were significantly lower compared to those from regions/states that had not yet reopened at the same time points, both in the student population (overall average treatment effect for the treated subpopulation [O-ATT]: -0.80 [95% CI: -0.94;-0.66] for Italy; O-ATT-0.30 [95% CI: -0.36;-0.23] for Germany) and the adult population (O-ATT: -0.04 [95% CI: -0.07;-0.01] for Italy; O-ATT: -0.07 [95% CI: -0.11;-0.03] for Germany). In Portugal, there was a significant decreasing trend in Rc following school reopenings compared to the pre-reopening period (sustained effect: -0.03 [95% CI: -0.04; -0.03] in students; -0.02 [95% CI: -0.03; -0.02] in adults). CONCLUSIONS: We found no evidence of a causal relationship between school reopenings in autumn 2022 and Omicron SARS-CoV-2 transmission.

Dietary protein is the strong predictor of coronary artery disease; a data mining approach.

BACKGROUNDS: Coronary artery disease (CAD) is the major cause of mortality and morbidity globally. Diet is known to contribute to CAD risk, and the dietary intake of specific macro- or micro-nutrients might be potential predictors of CAD risk. Machine learning methods may be helpful in the analysis of the contribution of several parameters in dietary including macro- and micro-nutrients to CAD risk. Here we aimed to determine the most important dietary factors for predicting CAD. METHODS: A total of 273 cases with more than 50% obstruction in at least one coronary artery and 443 healthy controls who completed a food frequency questionnaire (FFQ) were entered into the study. All dietary intakes were adjusted for energy intake. The QUEST method was applied to determine the diagnosis pattern of CAD. RESULTS: A total of 34 dietary variables obtained from the FFQ were entered into the initial study analysis, of these variables 23 were significantly associated with CAD according to t-tests. Of these 23 dietary input variables, adjusted protein, manganese, biotin, zinc and cholesterol remained in the model. According to our tree, only protein intake could identify the patients with coronary artery stenosis according to angiography from healthy participant up to 80%. The dietary intake of manganese was the second most important variable. The accuracy of the tree was 84.36% for the training dataset and 82.94% for the testing dataset. CONCLUSION: Among several dietary macro- and micro-nutrients, a combination of protein, manganese, biotin, zinc and cholesterol could predict the presence of CAD in individuals undergoing angiography.

Optimal control therapy and vaccination for special HIV-1 model with delay.

In this paper, we consider a four dimensional model of the human immunodeficiency virus-1 (HIV-1) with delay, which is an extension of some three dimensional models. We approach the treatment problem by adding two controllers to the system for inhibiting viral production. The optimal controller [Formula: see text] is considered for vaccine and [Formula: see text] for the drug. The Pontryagin maximum principle with delay is used to characterize these optimal controls. At the end, numerical results are presented to illustrate the optimal solutions. The validity of the model was confirmed by proper semi-quantitative simulation of some clinical data. The model was used to predict the possible beneficial effects of vaccine and anti-retroviral drug administration in HIV-1 disease.