Despite the progress of its management, COVID-19 maintains an ominous condition which constitutes a threat, especially for the susceptible population. The cardiac injury occurs in approximately 30% of COVID-19 infections and is associated with a worse prognosis. The clinical presentation of cardiac involvement can be COVID-19-related myocarditis. Our review aims to summarise current evidence about that complication. The research was registered at PROSPERO (CRD42022338397). We performed a systematic analysis using five different databases, including i.a. MEDLINE. Further, the backward snowballing technique was applied to identify additional papers. Inclusion criteria were: full-text articles in English presenting cases of COVID-19-related myocarditis diagnosed by the ESC criteria and patients over 18 years old. The myocarditis had to occur after the COVID-19 infection, not vaccination. Initially, 1588 papers were screened from the database search, and 1037 papers were revealed in the backward snowballing process. Eventually, 59 articles were included. Data about patients’ sex, age, ethnicity, COVID-19 confirmation technique and vaccination status, reported symptoms, physical condition, laboratory and radiological findings, applied treatment and patient outcome were investigated and summarised. COVID-19-related myocarditis is associated with the risk of sudden worsening of patients’ clinical status, thus, knowledge about its clinical presentation is essential for healthcare workers.

With its complicated pathophysiology, high incidence and prevalence, heart failure remains a major public concern. In hopes of improving diagnosis, treatment and prognosis, the utility of many different biomarkers is researched vigorously around the world. In this review, biomarkers of myocardial remodeling and fibrosis (galectin-3, soluble isoform of suppression of tumorigenicity 2, matrix metalloproteinases, osteopontin, interleukin-6, syndecan-4, myostatin, procollagen type I C-terminal propeptide, procollagen type III N-terminal propeptide, vascular endothelial growth factor, nitric oxidase synthetase and asymmetric dimethylarginine), myocyte injury (heart-type fatty acid-binding protein, glutathione S-transferase P1 and heat shock protein 60), as well as iron metabolism (ferritin, transferrin saturation, soluble transferrin receptor and hepcidin), are considered in terms of possible clinical applicability and significance. Our short review consists of a summary of the aforementioned cardiovascular biomarkers’ clinical relevance and perspectives.

AimsDiuretic response in heart failure is blunted when compared to healthy individuals, but the pathophysiology underlying this phenomenon is unclear. We aimed to investigate whether the diuretic resistance mechanism is related to insufficient furosemide tubular delivery or low tubular responsiveness.Methods and resultsWe conducted a prospective, observational study of 50 patients with acute heart failure patients divided into two groups based on previous furosemide use (furosemide naïve: n = 28 [56%] and chronic furosemide users: n = 22 [44%]). Each patient received a protocol‐derived, standardized furosemide dose based on body weight. We measured diuretic response and urine furosemide concentrations. The furosemide naïve group had significantly higher urine volumes and natriuresis when compared to chronic users at all timepoints (all p < 0.05). Urine furosemide delivery was similar in furosemide naïve versus chronic users after accounting for differences in estimated glomerular filtration rate (28.02 [21.03–35.89] vs. 29.70 [18.19–34.71] mg, p = 0.87). However, the tubular response to delivered diuretic was dramatically higher in naïve versus chronic users, that is the urine volume per 1 μg/ml of urine furosemide at 2 h was 148.6 ± 136.1 versus 50.6 ± 56.1 ml (p = 0.005).ConclusionsPatients naïve to furosemide have significantly better diuresis and natriuresis when compared to chronic furosemide users. The blunted diuretic response in patients with chronic loop diuretic exposure is driven by decreased tubular responsiveness rather than insufficient furosemide tubular delivery.

Acute heart failure (AHF) is a life-threatening, heterogeneous disease requiring urgent diagnosis and treatment. The clinical severity and medical procedures differ according to a complex interplay between the deterioration cause, underlying cardiac substrate, and comorbidities. This study aimed to analyze the natural phenotypic heterogeneity of the AHF population and evaluate the possibilities offered by clustering (unsupervised machine-learning technique) in a medical data assessment. We evaluated data from 381 AHF patients. Sixty-three clinical and biochemical features were assessed at the admission of the patients and were included in the analysis after the preprocessing. The K-medoids algorithm was implemented to create the clusters, and optimization, based on the Davies-Bouldin index, was used. The clustering was performed while blinded to the outcome. The outcome associations were evaluated using the Kaplan-Meier curves and Cox proportional-hazards regressions. The algorithm distinguished six clusters that differed significantly in 58 variables concerning i.e., etiology, clinical status, comorbidities, laboratory parameters and lifestyle factors. The clusters differed in terms of the one-year mortality (p = 0.002) and two-year mortality (p = 0.002). Using the clustering techniques, we extracted six phenotypes from AHF patients with distinct clinical characteristics and outcomes. Our results can be valuable for future trial constructions and customized treatment.

Sonoporation is a rapidly developing novel technique serving for drug delivery and non-viral gene therapy. It is based on the interaction between microbubbles located in the surrounding of a cell and its membrane. The interaction is obtained by excitation of microbubbles with ultrasounds. This leads to reversible cell membrane poration. Depending on the intensity of ultrasounds, structure of microbubbles used in an experiment and different environmental factors, microbubbles can interact in two manners. First, in lower ultrasound intensities, stable cavitation-regular microbubbles oscillations due to changes in the environment pressure. Microbubbles have to be very close to a cell membrane, therefore, they are usually targeted to an antigen located on the cell membrane by antibodies. Consequently, microbubbles push and pull on the cell membrane and create microstreaming around it causing its disruption. Second, inertial cavitation, where in contrary to the previous one, oscillations cause rapid collapse of microbubbles, which creates shock waves and microjets for the same purpose. No matter in which manner prorated, cells tend to reseal their disrupted cell membrane. Ca2+ ions play a crucial role in the process as well as endo exocytosis. Sonoporation has proved to be an effective modality against different diseases, including variety of cancer types in of both laboratory and clinical studies.

Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management.

Neurohormone activation plays an important role in Acute Heart Failure (AHF) pathophysiology. Serum osmolarity can affect this activation causing vasopressin excretion. The role of serum osmolarity and vasopressin concentration and its interaction remain still unexplored in AHF. The objective of our study was to evaluate the relationship of serum osmolarity with clinical parameters, vasopressin concentration, in-hospital course, and outcomes in AHF patients. The study group consisted of 338 AHF patients (male (76.3%), mean age of 68 ± 13 years) with serum osmolarity calculated by the equation: 1.86 × sodium [mmol/L] + (glucose [mg/dL]/18) + (urea [mg/dL]/2.8) + 9 and divided into osmolarity quartiles marked as: low: <287 mOsm/L, intermediate low: 287–294 mOsm/L, intermediate high: 295–304 mOsm/L, and high: >304 mOsm/L. There was an increasing age gradient in the groups and patients differed in the occurrence of comorbidities and baseline clinical and laboratory parameters. Importantly, analysis revealed that vasopressin presented a linear correlation with osmolarity (r = −0.221, p = 0.003) and its concentration decreased with quartiles (61.6 [44.0–81.0] vs. 57.8 [50.0–77.3] vs. 52.7 [43.1–69.2] vs. 45.0 [30.7–60.7] pg/mL, respectively, p = 0.034). This association across quartiles was observed among de novo AHF (63.6 [55.3–94.5] vs. 58.0 [50.7–78.6] vs. 52.0 [46.0–58.0] vs. 38.0 [27.0–57.0] pg/mL, respectively, p = 0.022) and was not statistically significant in patients with acute decompensated heart failure (ADHF) (59.5 [37.4–80.0] vs. 52.0 [38.0–74.5] vs. 57.0 [38.0–79.0] vs. 50.0 [33.0–84.0] pg/mL, respectively, p = 0.849). The worsening of renal function episodes were more frequent in quartiles with higher osmolarity (4 vs. 2 vs. 13 vs. 11%, respectively, p = 0.018) and patients that belonged to the quartiles with low and high osmolarity were characterized more often by incidence of worsening heart failure (20 vs. 9 vs. 10 vs. 22%, respectively, p = 0.032). There was also a U-shape distribution in relation to one-year mortality (31 vs. 19 vs. 23 vs. 37%, respectively, p = 0.022). In conclusion, there was an association of serum osmolarity with clinical status and both in-hospital and out-of-hospital outcomes. Moreover, the linear dependence between vasopressin concentration and serum osmolarity in the AHF population was identified and was driven mainly by patients with de novo AHF which suggests different pathophysiological paths in ADHF and AHF de novo.

The aim of this research was to examine the prevalence of hyperventilation (defined by pCO2 value) among acute heart failure (AHF) patients and to link it with potential triggers and prognosis. All patients underwent dyspnea severity assessment and capillary blood examination on hospital admission and during hospitalization. Out of 241 AHF patients, 57(24%) were assigned to low pCO2 group (pCO2 ≤ 30 mmHg) and 184 (76%) to normal pCO2 group (pCO2 > 30 mmHg). Low pCO2 group had significantly lower HCO3- (22.3 ± 3.4 vs 24.7 ± 2.9 mmol/L, p < 0.0001) and significantly higher lactate level (2.53 ± 1.6 vs 2.14 ± 0.97 mmol/L, p = 0.03). No differences between groups were observed in respect to the following potential triggers of hyperventilation: hypoxia (sO2 92.5 ± 5.2 vs 92 ± 5.6% p = 0.57), infection (CRP 10.5[4.9–26.4]vs 7.15[3.45–17.35] mg/L, p = 0.47), dyspnea severity (7.8 ± 2.3vs 8.0 ± 2.3 points, p = 0.59) and pulmonary congestion (82.5 vs 89.1%, p = 0.19), respectively. Low pCO2 value was related to an increased 4-year all-cause mortality hazard ratio (HR) (95% CI) 2.2 (1.3–3.6); p = 0.002 and risk of death and of rehospitalization for HF, HR (95% CI) 2.0 (1.3–3.0); p = 0.002. Hyperventilation is relatively frequent in AHF and is related to poor prognosis. Low pCO2 was not contingent on expected potential triggers of dyspnea but rather on tissue hypoperfusion.