Introductions. Inflammation plays a crucial role in the occurrence and progression of atherosclerosis. Recent studies have explored the clinical effects of inflammatory reactions in the development of coronary heart disease. They found that different subtypes of leukocytes—specifically neutrophils, lymphocytes, and monocytes—provide more predictive value for assessing the risk of cardiovascular disease than just the total leukocyte count. Additionally, several studies have reported hematological abnormalities in COVID-19 patients. These abnormalities include an increase in white blood cell count, as well as a decrease in red blood cell count and hemoglobin levels. Such alterations are associated with a higher risk of severe disease and poorer outcomes. Smoking is also a recognized risk factor, as noted in the Framingham Heart Study. Smokers face an increased risk of myocardial infarction or sudden cardiac events.

Aim. To evaluate the levels of leukocyte inflammatory markers in patients with unstable angina and post-COVID-19 syndrome, based on smoking status.

Materials and Methods. The study involved 147 patients with unstable angina aged between 35 and 76 years, with a mean age of 60.32 ± 0.66 years. Among the participants, 17.69% (n=26) were women, while 82.31% (n=121) were men. The presence of post-COVID syndrome was assessed using the POSTCOVID-19 Functional Status Scale, which allowed for the division of patients into two groups: group (I) comprised 87 patients (59.18%) with post-COVID syndrome, and group (II) included 60 individuals (40.81%) without post-COVID syndrome. Each group was further categorized into subgroups based on smoking status: subgroup IA and IIA consisted of smokers, while subgroup IB and IIB comprised non-smokers. The study measured several leukocyte inflammatory markers, including the Neutrophil-to-Lymphocyte Ratio (NLR), Monocyte-to-Lymphocyte Ratio (MLR), Systemic Immune Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), and Aggregate Index of Systemic Inflammation (AISI). Comparisons were made between the subgroups: IA and IIB, IA and IIA, as well as IB and IIB, taking into account the presence or absence of post-COVID syndrome and smoking status.

Results and discussion. The data obtained revealed that mean Neutrophil-Lymphocyte Ratio (NLR) levels were significantly higher in the smoking subgroup experiencing post-COVID syndrome (designated as IA) compared to the non-smoking subgroup without post-COVID syndrome (designated as IIB). Specifically, NLR levels in subgroup IA were 50% greater, measuring 3.47±0.85×10⁹/L, compared to 1.73±0.12×10⁹/L in subgroup IIB (p<0.03). Additionally, the average Monocyte-Lymphocyte Ratio (MLR) was also significantly elevated in subgroup IA by 37.9%, with values of 0.29±0.04×10⁹/L for IA versus 0.18±0.02×10⁹/L for IIB (p<0.03).  The aggregate indices of leukocyte inflammation, including Systemic Inflammatory Index (SII), Systemic Inflammatory Response Index (SIRI), and Aggregate Immune Score Index (AISI), were significantly higher in subgroup IA, ranging from 52% to 62% compared to subgroup IIB. Specifically, SII was 52.7% higher (803.81±163.64×10⁹/L for IA vs. 380.42±34.78×10⁹/L for IIB, p<0.009), SIRI was 60.8% higher (2.02±0.60×10⁹/L for IA vs. 0.80±0.09×10⁹/L for IIB, p<0.02), and AISI was 62.7% higher (466.64±115.80×10⁹/L for IA vs. 174.06±24.32×10⁹/L for IIB, p<0.01). When comparing smokers with post-COVID syndrome (IA) to smokers without post-COVID syndrome (IIA), MLR was 37.93% higher in the IA subgroup (0.29±0.04×10⁹/L for IA vs. 0.18±0.02×10⁹/L for IIA, p<0.02). SIRI was 55.9% higher (803.81±163.64×10⁹/L for IA vs. 380.42±34.78×10⁹/L for IIA, p<0.05), and AISI was 53.99% higher (466.64±115.80×10⁹/L for IA vs. 174.06±24.32×10⁹/L for IIA, p<0.03). Among non-smokers, individuals with post-COVID syndrome (IB) exhibited significantly higher levels of inflammatory leukocyte markers compared to non-smokers without post-COVID syndrome (IIB), showing increases of 33% to 40%. Specifically, NLR was 34.2% higher (2.63±0.22×10⁹/L for IB vs. 1.73±0.12×10⁹/L for IIB, p<0.005), SII was 39.16% higher (625.26±57.12×10⁹/L for IB vs. 380.42±34.78×10⁹/L for IIB, p<0.001), SIRI was 33.88% higher (1.21±0.12×10⁹/L for IB vs. 0.80±0.09×10⁹/L for IIB, p<0.01), and AISI was 39.57% higher (288.05±32.20×10⁹/L for IB vs. 174.06±24.32×10⁹/L for IIB, p<0.006).

Conclusions. Patients with unstable angina who present with both post-COVID syndrome and a history of smoking (IA) demonstrate the highest average levels of leukocyte inflammatory markers. In comparison, smokers without post-COVID syndrome (IIA) and non-smokers in general (IB and IIB) exhibit lower levels. It is noteworthy that non-smokers with post-COVID syndrome (IB) still display elevated levels of leukocyte inflammatory markers relative to non-smokers without post-COVID syndrome (IIB). These results indicate that both post-COVID syndrome and smoking may independently exert pro-inflammatory effects, leading to a significantly enhanced inflammatory response, as reflected by increased average levels of leukocyte inflammatory markers in the affected subgroups. The interplay between these factors serves to amplify their individual effects, culminating in a markedly pronounced inflammatory response.

Keywords: ischemic heart disease, unstable angina, COVID-19, post-COVID syndrome, smoking, inflammation markers.

Background. Liver involvement secondary to multiple myeloma is a rare and uncommon radiologic finding. Such extraosseous secondary lesions as well as tongue involvement require pathohistological confirmation to prevent misdiagnosis. Clinical and laboratory diagnostics are challenging in patients with COVID-19 and underlying multiple myeloma and its secondary lesions, leading to difficulties in treatment and outcomes.
Case Report. A 64-year-old male patient, not vaccinated against COVID-19, with a history of multiple myeloma presented with symptoms of headache, fatigue, dyspnea, cough, and fever. The patient’s medical history was intricate, involving cholecystectomy and a diagnosis of multiple myeloma, which was
subsequently treated with chemotherapy and radiation therapy. Additionally, uncommon liver and tongue involvement secondary to multiple myeloma was found. Upon admission, the patient’s peripheral oxygen saturation was 90%, accompanied by increasing shortness of breath and a respiratory rate of 26 breaths per minute. A positive COVID-19 test was recorded. A lung computed tomography revealed bilateral multifocal areas of ground-glass opacity and consolidation, encompassing the entire pulmonary regions, corresponding to CO-RADS 6. The patient was admitted to the intensive care unit. Despite initiating oxygen support and symptomatic therapy, the patient’s death occurred. Autopsy confirmed the development of severe acute respiratory distress syndrome and bilateral hemorrhagic pneumonia, with multiple myeloma as a contributing factor.
Conclusions. This case report highlighted the rare occurrence of secondary liver involvement in multiple myeloma, characterized by nodules with distinct imaging features. It underscored the importance of identifying coexisting lesions, such as tongue involvement, and the diagnostic challenges they pose. Additionally, the case emphasized the need for comprehensive clinical assessment in patients with concurrent COVID-19 and underlying multiple myeloma, as it may lead to the development of acute respiratory distress syndrome.

UDC 616.379-008.64]:617.735-005-079-052:575

Abstract. Background. It is known that in diabetic retinopathy (DR), impaired transforming growth factor β1 (TGF-β1) signaling is accompanied by pathological angiogenesis, disruption of the blood-eye barrier, activation of inflammation and tissue fibrosis. The purpose of the study was to establish the relationship between the content of TGF-β1 in blood serum and intraocular fluid (IOF) and the progression of DR in type 2 diabetes mellitus (T2DM) using neural network modeling. Materials and methods. The study included the results of the examination of 102 people with T2DM, who were divided into 3 groups according to the stages of DR: the first one — non-proliferative DR (NPDR, 35 people), the second one — preproliferative (PPDR, 34 people) and the third one — proliferative (PDR, 33 people). The control group consisted of 61 individuals. The patients underwent standard ophthalmic examinations. TGF-β1 in blood serum and IOF was evaluated by enzyme-linked immunosorbent assay (Invitrogen Thermo Fisher Scientific, USA). Statistical analysis of the results was performed using the MedCalc software package (MedCalc SoftWare bvba, 1993–2013) and a two-layer neural network model with a linear postsynaptic potential function. Results. Using the genetic selection algorithm, 3 features were identified that were associated with DR: diabetes compensation and TGF-β1 content in blood and IOF. T2DM was compensated in 38 (37.3 %) patients, while in 64 cases (62.7 %), it was uncompensated. The proportion of the latter was higher in PDR than in NPDR and PPDR (p < 0.05). In PDR, the TGF-β1 content in IOF was significantly higher than in NPDR and PPDR (p < 0.05). A three-factor classification model was created on the identified features, which included a system of equations that predicted PDR with 100% accuracy. The overall prediction accuracy of the model was 88.2 % (95% CI 80.4–93.8 %). Conclusions. In this study, the value of indicators such as diabetes compensation and TGF-β1 content in serum and IOF for the progression of DR to PDR was shown using the method of neural network modeling. Keywords: proliferative diabetic retinopathy; diabetes mellitus; transforming growth factor β1; intraocular fluid; neural network modeling

Purpose: To study and compare the immune response and neopterin levels in the blood in experimental autoimmune uveitis (EAU).

Methods: A model of EAU was created in 30 Chinchilla rabbits. Intravenous and intravitreal injections of normal horse serum were administered for this purpose. Clinical examinations and blood tests were conducted on days 3, 7, 10, 14, and 21. The blood investigation included the determination of neopterin (NP) level, white blood cell counts, lymphocytes, CD3+ , CD4+ , CD8+ , and CD16+ .

Results: The peak in white blood cell count was observed on days 7 and 10 (6.4 ± 0.4 g/L and 6.0 ± 0.3 g/L, respectively), lymphocytes on day 3 (68.3% ± 2.4%, 3.0 ± 0.2 g/L), CD3 + on day 7 (64.9% ± 3.1%, 2,032.5 ± 91.2 cells/µL), CD4 + and CD16 + on day 10 (54.6% ± 3.8%, 2,462.3 ± 60.7 cells/µL and 21.8% ± 1.8%, 691.2 ± 37.1 cells/µL, respectively). All these values did not return to the initial ones. There was a gradual decrease in the CD8 + count from day 3 (12.5% ± 1.1%, 142.8 ± 9.1 cells/µL) with a subsequent gradual return towards normal levels by day 21. NP levels incresed on day 3 (5.2 ± 0.7 nmol/L), sustained on day 7 (5.2 ± 0.8 nmol/L), and started to decrease from day 10 (4.25 ± 1.7 nmol/L) to 2.3 ± 0.5 nmol/L on day 21. The highest correlation was observed between clinical manifestations and NP with a correlation coeffient of 0.799 (95% confidence interval, 0.719–0.858), which was significantly stronger (p < 0.05) than the correlations with other immune response markers. Conclusions: During the modeling of EAU, there is an active immune response and a rapid reaction of NP on inflammation. NP is a significantly more sensitive marker of intraocular inflammation than the immune response. It can serve as a predictor of the onset and development of EAU.

Key Words: Animal, Blood, Immunity, Neopterin, Uveitis