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(1) Background: Heart rate variability (HRV) has been investigated in the context of ST-segment elevation myocardial infarction (STEMI). This study contributes to the field by assessing short-term HRV during primary percutaneous coronary intervention (PCI) using wearable technology, providing real-time insights into autonomic function. (2) Methods: This single-center, observational cohort study included 104 STEMI patients undergoing primary percutaneous coronary intervention (PCI). HRV parameters (including SDNN, RMSSD, pNN50, HF, SD1, and SD2/SD1 ratio) were measured using a wearable device (Empatica E4 wristband, CE certified). Measurements were taken throughout the entire duration of the primary PCI, as well as specifically during the initial 5 min and the final 5 min of the procedure. The association between HRV parameters and adverse outcomes, including in-hospital mortality and in-hospital major adverse cardiovascular events (MACE), were assessed. (3) Results: HRV parameters significantly decreased after myocardial revascularization, particularly SDNN, RMSSD, pNN50, HF, SD1, and SD2/SD1 ratio. Significant associations were found between reduced SD2/SD1 ratio, approximate entropy, and adverse outcomes, including increased in-hospital mortality and in-hospital MACE (respectively, p = 0.007, p = 0.017 and p = 0.006 and p = 0.005). The SD2/SD1 ratio was significantly lower in patients who died during the hospital stay (p = 0.008) compared to survivors. Approximate entropy was also significantly lower in deceased patients (p = 0.019). (4) Conclusions: Real-time HRV monitoring using wearable technology offers valuable data regarding dynamic physiological changes during primary PCI. Further studies are required to validate these preliminary results and to explore their potential implications for clinical practice.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by periods of exacerbation and remission, making disease monitoring and management challenging. Endoscopy, the gold standard for assessing disease activity and severity, involves invasive procedures and is associated with patient discomfort and risks. Using machine learning (ML) to combine fecal calprotectin with other clinical or biological tests can significantly enhance the non-invasive prediction of endoscopic disease activity (EDA) in UC. Aim: To prove that by fusing fecal calprotectin with other clinical data into an ML model, the performance of the non-invasive prediction of EDA can be significantly improved. Methods: We conducted a prospective, observational, single-center study encompassing 103 patients diagnosed with UC. We employed multilayer perceptron models as the core ML algorithm for predicting EDA. For the constructed models, we utilized the varImp function from the caret library in R to assess the significance of each variable in predicting the outcome. Results: Calprotectin as a sole predictor obtained an accuracy of 70% and an area under the curve (AUC) of 0.68. Combining calprotectin with the list of selected predictors that were fed to the MLP models improved accuracy and the AUC. The accuracy of the algorithm on the test set was 85%. Similarly, the AUC increased to 0.93. This is the first study to propose the use of calprotectin as a predictor in an ML model to estimate UC endoscopic disease activity. Conclusion: The deployment of this ML model can furnish doctors and patients with valuable evaluation of endoscopic disease activity which can be highly beneficial for individuals with UC who need long-term treatment.

Inflammatory bowel disease (IBD) comprises two types of chronic intestinal disorders: Crohn’s disease and ulcerative colitis. In long-standing ulcerative colitis disease activity, histological persistent inflammation has been linked to an increased risk of relapse, and long-term corticosteroid use, even when endoscopic remission is reached. In Crohn’s disease, the discontinuous nature of lesions and transmural inflammation have limited the standardized histological assessment. The current evidence from research proposes that besides clinical and endoscopic healing, the achievement of histological healing constitutes an endpoint to assess disease activity and remission in IBD patients concerning better long-term disease outcomes. Histological alterations may persist even in the absence of endoscopic lesions. For these reasons, new advanced techniques promise to revolutionize the field of IBD by improving the endoscopic and histologic assessment, disease characterization, and ultimately patient care, with an established role in daily practice for objective assessment of lesions. This review outlines the importance of including microscopic evaluation in IBD, highlighting the clinical benefits of a deep state of disease remission using validated diagnostic methods and scoring systems for daily clinical practice.

The optimization of locomotive drivers’ scheduling in rail freight transportation comes as a necessity for minimizing economic expenses and training investments. The Ferodata AI engine, an artificial intelligence (AI)/machine learning (ML) software module, developed by our team, has integrated a supervised random forest model that automatically assigns conductors to freight transportation orders based on the data about locomotive driver’s tiredness score, distance of the driver to the departure point of a transportation order, driver availability, and circulation history. The model proposed by us obtained very good performance metrics on the train set (accuracy: 95%, AUC: 0.9905) and reasonably good and encouraging performance on the test set (accuracy: 84%, AUC: 0.8357). After rigorous testing and validation on external and larger datasets, the automated optimization of locomotive driver assignments could bring operational efficiency, cost savings, regulatory compliance, and improved safety to scheduled rail freight transports.

Sentiment Analysis (SA) is a novel branch of Natural Language Processing (NLP) that measures emotions or attitudes behind a written text. First applications of SA in healthcare were the detection of disease-related emotional polarities in social media. Now it is possible to extract more complex attitudes (rank attitudes from 1 to 5, assign appraisal values, apply multiple text classifiers) or feelings through NLP techniques, with clear benefits in cardiology; as emotions were proved to be veritable risk factors for the development of cardiovascular diseases (CVD). Our narrative review aimed to summarize the current directions of SA in cardiology and raise the awareness of cardiologists about the potentiality of this novel domain. This paper introduces the readers to basic concepts surrounding medical SA and the need for SA in cardiovascular healthcare. Our synthesis of the current literature proved SA's clinical potential in CVD. However, many other clinical utilities, such as the assessment of emotional consequences of illness, patient-physician relationship, physician intuitions in CVD are not yet explored. These issues constitute future research directions, along with proposing detailed regulations, popularizing health social media among elders, developing insightful definitions of emotional polarity, and investing research into the development of powerful SA algorithms.

Hepatic diseases pose a significant public health concern. Regardless of the severity of hepatic fibrosis, treatment is recommended for all chronic hepatitis C virus (HCV) subjects. However, fibrosis and steatosis assessment remains crucial for evaluating the prognosis, progression, and hepatic disease monitoring, particularly following the treatment with direct-acting antivirals (DAAs). The aim of our study was to evaluate the impact of metabolic factors and the extent of hepatic fibrosis and fat accumulation in chronic HCV infection subjects. Additionally, another objective was to investigate modifications regarding fibrosis and steatosis three months after a successful sustained viral response (SVR). A total of 100 patients with compensated cirrhosis and chronic hepatitis C (CHC) were included in our study. These patients received treatment with DAA and underwent Fibromax assessment before and three months post SVR. After DAA treatment, a significant decrease was observed in the degree of hepatic fibrosis and hepatic steatosis. This regression was evident three months following the achievement of SVR. Chronic viral hepatitis C may trigger risk factors for metabolic syndromes, such as obesity and type 2 diabetes mellitus. Conclusions: It is crucial to monitor metabolic factors and take timely measures to prevent or treat metabolic syndrome in patients with chronic viral hepatitis C.

Background and Objectives: In the early period after liver transplantation, patients are exposed to a high rate of complications and several scores are currently available to predict adverse postoperative outcomes. However, an ideal, universally accepted and validated score to predict adverse events in liver transplant recipients with hepatitis C is lacking. Therefore, we aimed to establish and validate a machine learning (ML) model to predict short-term outcomes of hepatitis C patients who underwent liver transplantation. Materials and Methods: We conducted a retrospective observational two-center cohort study involving hepatitis C patients who underwent liver transplantation. Based on clinical and laboratory parameters, the dataset was used to train a deep-learning model for predicting short-term postoperative complications (within one month following liver transplantation). Adverse events prediction in the postoperative setting was the primary study outcome. Results: A total of 90 liver transplant recipients with hepatitis C were enrolled in the present study, 80 patients in the training cohort and ten in the validation cohort, respectively. The age range of the participants was 12–68 years, 51 (56,7%) were male, and 39 (43.3%) were female. Throughout the 85 training epochs, the model achieved a very good performance, with the accuracy ranging between 99.76% and 100%. After testing the model on the validation set, the deep-learning classifier confirmed the performance in predicting postoperative complications, achieving an accuracy of 100% on unseen data. Conclusions: We successfully developed a ML model to predict postoperative complications following liver transplantation in hepatitis C patients. The model demonstrated an excellent performance for accurate adverse event prediction. Consequently, the present study constitutes the foundation for careful and non-invasive identification of high-risk patients who might benefit from a more intensive postoperative monitoring strategy.

According to new research, a possible association between inflammatory bowel disease (IBD) and an increased risk of ischemic heart disease (IHD) has been demonstrated, but this concern is still debatable. The purpose of this review is to investigate the link between IHD and IBD, as well as identify further research pathways that could help develop clinical recommendations for the management of IHD risk in IBD patients. There is growing evidence suggesting that disruption of the intestinal mucosal barrier in IBD is associated with the translocation of microbial lipopolysaccharides (LPS) and other endotoxins into the bloodstream, which might induce a pro-inflammatory cytokines response that can lead to endothelial dysfunction, atherosclerosis and acute cardiovascular events. Therefore, it is considered that the long-term inflammation process in IBD patients, similar to other chronic inflammatory diseases, may lead to IHD risk. The main cardiovascular risk factors, including high blood pressure, dyslipidemia, diabetes, smoking, and obesity, should be checked in all patients with IBD, and followed by strategies to reduce and manage early aggression. IBD activity is an important risk factor for acute cardiovascular events, and optimizing therapy for IBD patients should be followed as recommended in current guidelines, especially during active flares. Large long-term prospective studies, new biomarkers and scores are warranted to an optimal management of IHD risk in IBD patients.

Background and Objectives: The COVID-19 pandemic has had a considerable impact on inflammatory bowel disease (IBD) patients by limiting their access to medical services due to restrictions and the reorganization of the healthcare systems, which affects their quality of life (QoL). We aimed to assess the impact of the COVID-19 pandemic on the QoL of patients with IBD. Materials and Methods: We conducted a descriptive observational study, which included 90 adult patients diagnosed with IBD. The study sample consisted of two subgroups: a retrospective-pre-pandemic group (group A) and a prospective-pandemic group (group B). Group A included 45 IBD patients who were evaluated in 2018. Group B included 45 patients with confirmed diagnosis of IBD, evaluated between June and December 2021—the period of the COVID-19 pandemic (prospective), consecutively recruited. All the patients filled in a QoL assessment questionnaire—IBDQ-32. Subsequently, the two samples were comparatively assessed. Results: The average values of the IBDQ scores were significantly lower in 2021 compared to those recorded in 2018: 145.56 vs. 128.3 (p < 0.05). We also we found significant differences between the subscores: IBDQ1 (p = 0.043), IBDQ2 (p = 0.034), IBDQ3 (p = 0.045), IBDQ4 (p = 0.025). Conclusions: IBDQ scores were significantly lower in 2021 compared to 2018 (p < 0.05), showing that during the COVID-19 pandemic, patients with IBD had a more influenced QoL.

Herd immunity is necessary to control the coronavirus disease 2019 (COVID-19) pandemic. However, a low proportion of vaccinated people and low levels of vaccine acceptance have been noted in Eastern Europe. Our paper aimed to review the central attitudes associated with the hesitancy toward COVID-19 vaccination specific to Eastern European countries. The main Eastern European determinants of COVID-19 vaccine acceptance identified from the included studies are: public confidence in the vaccines’ safety and efficacy, vaccine literacy, and public trust in the government and the medical system. Each of these determinants is discussed along with possible improvement measures. Variables specific to Eastern Europe that predict the willingness to vaccinate have also been highlighted. The specific attitudes and their context as identified by our review should be incorporated into local public health programs, with the ultimate goal of reducing viral spreading, mutation emergence, and COVID-19 morbidity and mortality both within the borders of Eastern Europe and beyond.

The potential benefit of ferritin evaluation resides in its association with adverse outcomes in patients with various pathological conditions. We aimed to conduct the first systematic review evaluating the association between ferritin levels and adverse cardiovascular outcomes in patients with acute myocardial infarction (AMI) during short- or long-term follow-up. Seven studies investigating various endpoints (mortality, major adverse cardiovascular events-MACE, the decline of the left ventricular ejection fraction-LVEF, left ventricular aneurysm development-LVA) were included. AMI patients with low or increased ferritin values tended to have higher in-hospital and 30-day mortality rates. Low and high ferritin levels and chronic kidney disease were independently associated with increased risk of LVA formation. High ferritin concentrations were linked to an accentuated LVEF decline in ST-elevation myocardial infarction patients treated by percutaneous coronary intervention. Both low and high ferritin values were also associated with the duration of hospitalization in patients with AMI during hospital stay and at more extended follow-up. Ferritin evaluation represents a simple investigation that could identify high-risk patients with AMI who might benefit from closer monitoring and specific therapeutic interventions. These data should be confirmed in large trials in the context of currently available therapies for heart failure and AMI.