[Respiratory disorders of post-COVID-19 syndrome].

AIM: Assess the functional state of trespiratory system and effectiveness of therapeutic tactics for broncho-obstructive syndrome (BOS) in patients in the post-COVID period. MATERIALS AND METHODS: A two-center cohort prospective study included 10 456 and 89 patients, respectively. A comprehensive assessment of the respiratory system included clinical, laboratory and functional data, spirometry, body plethysmography, and a study of diffusive capacity of the lungs (DLCO). Therapy consisted of budesonide suspension or fixed combination beclomethasone dipropionate/formoterol (EMD BDP/FORM). RESULTS: The frequency of BOS in the cohort was 72% (7497 patients). In 13% (n=974) of cases, bronchial asthma was diagnosed for the first time, in 4.4% (n=328) - chronic obstructive pulmonary disease. Risk factors for the development and decrease in DLCO in the post-COVID period were identified. In the group of complex instrumental examination of lung function, the absence of violations of spirometric data and indicators determined by body plethysmography was determined. CONCLUSION: Risk factors for BOS in post-COVID period are atopy, a history of frequent acute respiratory infections, smoking, blood eosinophilia, moderate and severe forms of COVID-19. The advantage of a fixed combination of EMD BDP/FORM in MART mode compared with nebulized suspension budesonide + solution of salbutamol in treatment of BOS was shown. Risk factors for DLCO disorders were established: severe COVID-19, hospitalization in the intensive care unit, the need for additional oxygen therapy.

An application of QVAR dynamic connectedness between geopolitical risk and renewable energy volatility during the COVID-19 pandemic and Russia-Ukraine conflicts.

The article is the first to employ a quantile vector autoregression (QVAR) to identify the connectedness between geopolitical risks and energy volatility from January 1, 2015, to April 03, 2023. This paper is also the first to examine the mediating roles of uncertain events like the COVID-19 pandemic and the Russia-Ukraine conflict on this interlinkage. Dynamic connectedness is 29% in the short term and approximately 6% in the long term. Dynamic net total directional connectedness over a quantile also indicates that connectedness is very intense for both highly positive changes (above the 80% quantile) and negative changes (below the 20% quantile). In the short term, the geopolitical risks remained net receivers of shock, but they turned into net shock transmitters during 2020 in the long term. Clean energy, in the short term, transmits shocks to other markets, and it plays the same role in the long term. Crude oil was a net receiver of shocks during COVID-19 and turned into a net transmitter of shocks in early 2022. Dynamic net pairwise directional connectedness over a quantile suggests that uncertain events like the COVID-19 epidemic or the Russia-Ukraine conflict influence the dynamic interlinkages between geopolitical risks and renewable energy volatility and change their roles in the designed system. These findings are critical since they help authorities develop effective policies to lessen the vulnerabilities of these indicators and minimize how widely the renewable and non-renewable energy market is exposed to risk or uncertainty.

DrugRep-KG: Toward Learning a Unified Latent Space for Drug Repurposing Using Knowledge Graphs.

Drug repurposing or repositioning (DR) refers to finding new therapeutic applications for existing drugs. Current computational DR methods face data representation and negative data sampling challenges. Although retrospective studies attempt to operate various representations, it is a crucial step for an accurate prediction to aggregate these features and bring the associations between drugs and diseases into a unified latent space. In addition, the number of unknown associations between drugs and diseases, which is considered negative data, is much higher than the number of known associations, or positive data, leading to an imbalanced dataset. In this regard, we propose the DrugRep-KG method, which applies a knowledge graph embedding approach for representing drugs and diseases, to address these challenges. Despite the typical DR methods that consider all unknown drug-disease associations as negative data, we select a subset of unknown associations, provided the disease occurs because of an adverse reaction to a drug. DrugRep-KG has been evaluated based on different settings and achieves an AUC-ROC (area under the receiver operating characteristic curve) of 90.83% and an AUC-PR (area under the precision-recall curve) of 90.10%, which are higher than in previous works. Besides, we checked the performance of our framework in finding potential drugs for coronavirus infection and skin-related diseases: contact dermatitis and atopic eczema. DrugRep-KG predicted beclomethasone for contact dermatitis, and fluorometholone, clocortolone, fluocinonide, and beclomethasone for atopic eczema, all of which have previously been proven to be effective in other studies. Fluorometholone for contact dermatitis is a novel suggestion by DrugRep-KG that should be validated experimentally. DrugRep-KG also predicted the associations between COVID-19 and potential treatments suggested by DrugBank, in addition to new drug candidates provided with experimental evidence. The data and code underlying this article are available at https://github.com/CBRC-lab/DrugRep-KG.