Despite numerous studies conducted by various research teams, predicting long-term outcomes (known as Post-COVID-19 Syndrome, PCS) that may result from Coronavirus Disease 2019 (COVID-19) remains challenging. PCS affects over a million people, primarily those with comorbid conditions. Therefore, it is crucial to undertake research aimed at developing a predictive model for early diagnosis of PCS, which in turn would enable faster preventive actions. The aim of this study was to assess the value of measuring and attempt a quantitative evaluation using Enzyme-Linked Immunosorbent Assay (ELISA) tests of three non-serum proteins, whose presence in the blood during COVID-19 was associated with severe disease progression: neutrophil elastase (NE), calcium-binding protein S100B, and neuron-specific enolase (NSE). The concentrations of these proteins were measured in blood serum samples collected before the COVID-19 pandemic from (1) patients with type 2 diabetes (T2DM); (2) advanced stage diabetic nephropathy (NfT2DM); (3) a healthy group; and in blood serum samples collected two years after recovering from COVID-19 from patients with (4) T2DM and (5) NfT2DM. It was found that elevated levels of NE and NSE were significantly more common (p < 0.05) in patients with NfT2DM after recovering from COVID-19 compared to the other groups, while elevated levels of S100B were significantly more frequently observed in patients with T2DM after recovering from COVID-19 (p < 0.05). Demonstrating differences in the prevalence of NE, NSE, and S100B in individuals who recovered from COVID-19 with T2DM and NfT2DM makes these proteins important components of the developing predictive model for early detection of PCS. To our knowledge, this is the first study showing the significance of NE, NSE, and S100B in PCS in the context of T2DM and NfT2DM.

Due to the molecular mechanisms of action of antidiabetic drugs, they are considered to be effective in the treatment of both COVID-19 and the post-COVID-19 syndromes. The aim of this study was to determine the effect of administering insulin and metformin on the mortality of patients with type 2 diabetes (T2DM) with symptomatic COVID-19 with the use of logistic regression models. The association between death and insulin and metformin was weak and could not be included in the multivariate model. However, the interaction of both drugs with other factors, including remdesivir and low-molecular-weight heparin (metformin), age and hsCRP (insulin), modulated the odds of death. These interactions hint at multifaceted (anti-/pro-) associations of both insulin and metformin with the odds of death, depending on the patient’s characteristics. In the multivariate model, RDW-SD, adjusted with low-molecular-weight heparin treatment, age, sex and K⁺, was associated with mortality among patients with COVID-19 and T2DM. With a 15% increase in RDW-SD, the risk of death increased by 87.7%. This preliminary study provides the foundations for developing further, more personalized models to assess the risk of death in T2DM patients, as well as for identifying patients at an increased risk of death due to COVID-19.

Background: Even though coronary artery disease (CAD) is considered an independent risk factor of an unfavorable outcome of SARS-CoV-2-infection, the clinical course of COVID-19 in subjects with CAD is heterogeneous, ranging from clinically asymptomatic to fatal cases. Since the individual C₂HEST components are similar to the COVID-19 risk factors, we evaluated its predictive value in CAD subjects. Materials and Methods: In total, 2183 patients hospitalized due to confirmed COVID-19 were enrolled onto this study consecutively. Based on past medical history, subjects were assigned to one of two of the study arms (CAD vs. non-CAD) and allocated to different risk strata, based on the C₂HEST score. Results: The CAD cohort included 228 subjects, while the non-CAD cohort consisted of 1956 patients. In-hospital, 3-month and 6-month mortality was highest in the high-risk C₂HEST stratum in the CAD cohort, reaching 43.06%, 56.25% and 65.89%, respectively, whereas in the non-CAD cohort in the high-risk stratum, it reached: 26.92%, 50.77% and 64.55%. Significant differences in mortality between the C₂HEST stratum in the CAD arm were observed in post hoc analysis only for medium- vs. high-risk strata. The C₂HEST score in the CAD cohort could predict hypovolemic shock, pneumonia and acute heart failure during hospitalization, whereas in the non-CAD cohort, it could predict cardiovascular events (myocardial injury, acute heart failure, myocardial infract, carcinogenic shock), pneumonia, acute liver dysfunction and renal injury as well as bleedings. Conclusions: The C₂HEST score is a simple, easy-to-apply tool which might be useful in risk stratification, preferably in non-CAD subjects admitted to hospital due to COVID-19.

Thyroid cancer (TC), due to its heterogeneous nature, remains a clinical challenge. Many factors can initiate the carcinogenesis process of various types of TC, which complicates diagnosis and treatment. The presented review gathers current information on specific types of TC, taking into account the effects of the COVID-19 pandemic. It is likely that COVID-19 has influenced and continues to influence the function of the thyroid gland. A high percentage of patients with COVID-19 showing simultaneous pathological changes in the thyroid suggests that SARS-CoV-2 may disrupt the function of this gland and initiate pro-oxidative mechanisms, inflammatory states, and autoimmune diseases, thereby promoting the formation of neoplastic changes. Furthermore, changes in the expression of the ACE2, TMPRSS2, CLEC4M and DPP4 genes, observed in TC, also occur in COVID-19. Therefore, it is probable that the interaction of SARS-CoV-2 with thyroid cell receptors may initiate carcinogenesis in this gland. Additionally, some drugs used in TC therapy (e.g., levothyroxine) may increase the affinity of SARS-CoV-2 for cells, which could contribute to a more severe course of COVID-19 and the emergence of long-term symptoms (post-COVID-19). Moreover, the consequences of sanitary restrictions (limited access to medical services, reduction in endocrinological and oncological procedures) that took place in many countries during the COVID-19 pandemic may lead in the future to an increased number of missed diagnoses and the emergence of aggressive cancers.

Introduction. Patients with type 2 diabetes represent 50% of all sudden cardiac deaths. Disseminated arteriosclerotic lesions are the cause of vascular incidents that cause permanent disability resulting from lower limb amputations. Objectives. Our study was designed to investigate the relationship between asymmetrical dimethylarginine (ADMA), symmetric dimethylarginine (SDMA) plasma concentration and intima-media thickness (IMT) in subjects with diabetes mellitus without vascular complications (group A) and a group of diabetic patients diagnosed with diabetes micro- and macroangiopathy (group B). Patients and Method. The experimental groups included 42 diabetic patients. Group A - 22 patients (9 W and 13 M), free from vascular complications (mean age 55.83 +/- 7.37 years), group B - 20 patients (6 W, 14 M) with accompanying micro- and macropathic changes (mean age 63.80 +/- 8.79 years). Group C (n=22), the control group, consisted of healthy volunteers (12 W and 10 M), between the ages of 40 to 60 (mean age 51.16 +/- 6.39), selected in reference to the age and sex of the research group. The carotid artery intima-media complex thickness (IMT) was evaluated with the use of a duplex ultrasound. Conclusions. There was no correlation between ADMA and the maximal or mean IMT of the common carotid artery (CCA) and internal carotid artery (ICA). We demonstrated a correlation between SDMA concentration and CCA IMT. The results suggest that ICA IMT may serve as a marker of vascular complication among patients with diabetes.

Advanced age is known to be a predictor with COVID-19 severity. Understanding of other disease progression factors may shorten the time from patient admission to applied treatment. The Veterans Health Administration COVID-19 (VACO index) was assumed to additionally anticipate clinical results of patients hospitalized with a proven infection caused by the SARS-CoV-2 virus. Methods: The medical records of 2183 hospitalized patients were retrospectively analyzed. Patients were divided into four risk-of-death categories: low risk, medium risk, high-risk, and extreme risk depending on their VACO index calculation. Results: Significant differences in the mortality at the hospital after three months of discharge and six months after discharge were noticed. For the patients in the extreme-risk group, mortality reached 37.42%, 62.81%, and 78.44% for in-hospital, three months of discharge, and six months of discharge, respectively. The mortality marked as high risk reached 20.38%, 37.19%, and 58.77%. Moreover, the secondary outcomes analysis acknowledged that patients classified as extreme risk were more likely to suffer from cardiogenic shock, myocardial infarction, myocardial injury, stroke, pneumonia, acute kidney injury, and acute liver dysfunction. Patients at moderate risk were more often admitted to ICU when compared to other patients. Conclusions: The usage of the VACO index, combined with an appropriate well-defined medical interview and past medical history, tends to be a helpful instrument in order to predict short-term mortality and disease progression based on previous medical records.