Machine Learning Model Validated to Predict Outcomes of Liver Transplantation Recipients with Hepatitis C: The Romanian National Transplant Agency Cohort Experience.

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.

Impact of COVID-19 Pandemic on the Quality of Life of 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 2021­the period of the COVID-19 pandemic (prospective), consecutively recruited. All the patients filled in a QoL assessment questionnaire­IBDQ-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.

Treating a thrombotic giant dissecting renal artery aneurysm through ex-vivo vascular reconstructive surgery followed by kidney reimplantation.

Renal artery aneurysms, although rare, may give rise to complications both per se (due to the risk of thrombosis and subsequent wall rupture) and by impairment of the renal function (due to extrinsic compression and high blood pressure). We describe a paucisymptomatic young patient with acute thrombosis of a massive dissecting renal artery aneurysm, for which the successful treatment was performed through ex-vivo vascular surgery followed by autotransplantation of the reconstructed kidney. The aneurysm was described through abdominal echography, computed tomography angiography, and transfemoral transcatheter arterial angiography. It originated from an atypical branch emerging at 90 degrees from the left renal artery. After a short branching off, it degenerated into a dissected aneurysmal sac as large as half a kidney (outer diameter of 60 mm), compressing the lower pole of the left kidney and delaying the lower half nephrogram. Ex-vivo surgical exclusion of the aneurysm was successfully performed. The kidney was reimplanted in the left iliac fossa (termino-lateral anastomosis between the renal artery and external left iliac artery, termino-terminal ureteric anastomosis) with excellent postoperative outcomes. For most asymptomatic aneurysms, expectant treatment is a reasonable approach. However, interventional or surgical repair is indicated in certain circumstances depending on the size of the aneurysm and its natural history, rupture risk, and interventional/surgical risks. The renovascular hypertension, dissecting and thrombotic events, its giant size, the young fertile age, and the presence of the flank pain were all indicative of the need for aneurysm exclusion in our case.

Right Ventricular Functionality Following Hemodialysis Initiation in End-Stage Kidney Disease-A Single-Center, Prospective, Cohort Study.

Background and Objectives: Two-dimensional speckle tracking echocardiography (2D-STE) is viewed as an outstanding technique, competent of uncovering earlier subclinical myocardial anomalies compared to conventional echocardiography. A few endeavors adopted 2D-STE as a tool to estimate right ventricular (RV) function in subjects with end-stage kidney disease (ESKD). There is no published prospective study on an adult ESKD cohort exploring the consequences of commencing elective hemodialysis (HD) on RV behavior. Materials and Methods: We investigated the RV systolic function using traditional (tricuspid annular plane systolic excursion-TAPSE, RV fractional area change-FAC) and 2D-STE (RV free wall longitudinal strain-RVFWLS) parameters following the initiation of HD. We enrolled 79 consecutive patients with ESKD and assessed them in four steps-at baseline, before HD, and at 3, 6, and 12 months. Results: RVFWLS, FAC, and TAPSE values had a significant increase at 3, 6, and 12 months from baseline (p < 0.001) and a significant increase at 6 months from 3 months (p < 0.001). However, differences between 12 months and 6 months were not significant (p > 0.05) according to Dunn-Bonferroni post hoc tests. Seventeen deaths were recorded before the completion of the study. RVFWLS, FAC, and TAPSE values significantly decreased at 3 and 6 months in all 17 deceased patients, in clear opposition with the values survivors had. All the studied parameters had a significant prediction power on mortality (p < 0.001) having an outstanding performance: baseline-RVFWLS (AUC: 1.000 (95% C.I.: 1.000-1.000)), baseline-FAC (AUC: 0.974 (95% C.I.: 0.942-1.000)), and baseline-TAPSE (AUC: 0.962 (95% C.I.: 0.920-1.000). Conclusions: Our study is the first to investigate RV function by 2D-STE and correlate it with traditional methods in patients with ESKD before and after the initiation of HD. RV function was significantly ameliorated at 3, 6, and 12 months compared to the pre-HD values. FAC and RVFWLS gain an outstanding prognostic role on mortality in this population.

Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review.

Background and objectives: cardiovascular complications (CVC) are the leading cause of death in patients with chronic kidney disease (CKD). Standard cardiovascular disease risk prediction models used in the general population are not validated in patients with CKD. We aim to systematically review the up-to-date literature on reported outcomes of computational methods such as artificial intelligence (AI) or regression-based models to predict CVC in CKD patients. Materials and methods: the electronic databases of MEDLINE/PubMed, EMBASE, and ScienceDirect were systematically searched. The risk of bias and reporting quality for each study were assessed against transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) and the prediction model risk of bias assessment tool (PROBAST). Results: sixteen papers were included in the present systematic review: 15 non-randomized studies and 1 ongoing clinical trial. Twelve studies were found to perform AI or regression-based predictions of CVC in CKD, either through single or composite endpoints. Four studies have come up with computational solutions for other CV-related predictions in the CKD population. Conclusions: the identified studies represent palpable trends in areas of clinical promise with an encouraging present-day performance. However, there is a clear need for more extensive application of rigorous methodologies. Following the future prospective, randomized clinical trials, and thorough external validations, computational solutions will fill the gap in cardiovascular predictive tools for chronic kidney disease.

Anemia in Crohn's Disease-The Unseen Face of Inflammatory Bowel Disease.

Background and Objectives: Anemia is the most frequent complication of inflammatory bowel diseases. Clinically, anemia can affect important quality-of-life (QoL) components, such as exercise capacity, cognitive function, and the ability to carry out social activities. The disease activity has a significant impact on QoL, mainly due to clinical manifestations, which are more severe during the periods of disease activity. Our aim was to estimate the impact of anemia on QoL in patients with Crohn's disease. Material and Methods. We made a prospective study on 134 patients with Crohn's disease (CD) in a Romanian tertiary center. The CD diagnosis was established by colonoscopy and histopathological examination. In particular cases, additional examinations were required (small bowel capsule endoscopy, computed tomography enterography, and magnetic resonance enterography). Anemia was defined according to the World Health Organization's definition, the activity of the disease was assessed by Crohn's disease activity index (CDAI) score, and the QoL was evaluated by Inflammatory Bowel Disease Questionnaire 32 (IBDQ 32). Results: 44.8% patient had anemia, statistically related to the activity of the disease and corticoids use. We found a strong association between QoL and disease activity on all four sub-scores: patients with more severe activity had a significantly lower IBDQ (260.38 ± 116.96 vs. 163.85 ± 87.20, p = 0.001) and the presence of anemia (127.03 vs. 148.38, p = 0.001). In multiple regression analyses, both disease activity and anemia had an impact on the QoL. Conclusions: Anemia has high prevalence in the CD in northeastern region of Romania. Anemia was more common in female patients, in patients undergoing corticosteroid treatment, and in those with active disease. Both anemia and disease activity had a strong negative and independent impact on QoL.

"A Chain Only as Strong as Its Weakest Link": An Up-to-Date Literature Review on the Bidirectional Interaction of Pulmonary Fibrosis and COVID-19.

The COVID-19 pandemic rapidly became a worldwide healthcare emergency affecting millions of people, with poor outcomes for patients with chronic conditions and enormous pressure on healthcare systems. Pulmonary fibrosis (PF) has been cited as a risk factor for a more severe evolution of COVID-19, primarily because its acute exacerbations are already associated with high mortality. We reviewed the available literature on biochemical, pathophysiological, and pharmacological mechanisms of PF and COVID-19 in an attempt to foresee the particular risk of infection and possible evolution of PF patients if infected with SARS-COV-2. We also analyzed the possible role of medication and risk factors (such as smoking) in the disease's evolution and clinical course. We found out that there is a complexity of interactions between coexisting idiopathic pulmonary fibrosis/interstitial lung disease (ILD) and COVID-19 disease. Also, patients recovering from severe COVID-19 disease are at serious risk of developing PF. Smokers seem to have, in theory, a chance for a better outcome if they develop a severe form of COVID-19 but statistically are at much higher risk of dying if they become critically ill.

Determinants of acute kidney injury after cardiac surgery: a systematic review.

Acute kidney injury following cardiac surgery (CS-AKI) represents a severe postoperative complication, negatively impacting short-term and long-term mortality. Due to the lack of a specific treatment, effective prevention remains the most powerful tool to overcome the CS-AKI burden. Improving the preventive strategies is possible by establishing appropriate preoperative risk profiles. Various clinical models were proposed as a means to assist physicians in stratifying the risk of CS-AKI. However, these models are used for predicting severe forms of CS-AKI, while their predictive power for mild forms is insufficient. Our paper represents the first systematic approach to review all proposed preoperative risk factors and their predictive power. Our strategy is the starting point for selecting and comparing the predictive elements to be integrated into future risk models. Heart failure, chronic hyperglycemia, anemia, obesity, preoperative exposure to nephrotoxic drugs or contrast media, inflammation, proteinuria, and pre-existing kidney disease were systematically reviewed and were found to be associated with an increased risk of postoperative CS-AKI. As no externally validated and universally accepted risk models currently exist, the clinical judgment and a good knowledge of the preoperative risk factors in the light of new evidence may help personalize preoperative risk profiles as the cornerstone of prevention measures.

Pandemic lockdown, healthcare policies and human rights: integrating opposed views on COVID-19 public health mitigation measures.

The issue of the COVID-19 pandemic occupies the agenda of the whole world. The pivot of this pandemic is a crucial element that has become almost as important as the virus itself, namely the lockdown. Although, the rationale for lockdown is well-sustained by strong epidemiological arguments, exploring the 'other' unwanted consequences of the contemporary COVID-19 pandemic is mandatory for coagulating a robust agreed position against the numerous problems generated by the SARS-CoV-2 virus. Starting from the rationale of the lockdown, in this paper we explored and exposed the other consequences of the COVID-19 pandemic measures such as the use or abuse of human rights and freedom restrictions, economic issues, marginalized groups and eclipse of all other diseases. Our scientific attempt is to coagulate a stable position and integrate current opposing views by advancing the idea that rather than applying the uniform lockdown policy, one could recommend instead an improved model targeting more strict and more prolonged lockdowns to vulnerable risk/age groups while enabling less stringent measures for the lower-risk groups, minimizing both economic losses and deaths. Rigorous (and also governed by freedom) debating may be able to synchronize the opposed perspectives between those advocating an extreme lockdown (e.g., most of the epidemiologists and health experts), and those criticizing all restrictive measures (e.g., economists and human rights experts). Confronting the multiple facets of the public health mitigation measures is the only way to avoid contributing to history with yet another failure, as seen in other past epidemics.

The Second Wave of COVID-19 Pandemic Strikes during the Flu Season: An Awareness Perspective.

Coinfection with both SARS-CoV-2 and influenza viruses seems to be a real and severe problem. However, coinfection is far from a simple matter, and cannot be considered having more unfavorable outcomes as a direct consequence. In reality, the aftermath is powerfully nuanced by the presence of risk factors and specific molecular mechanisms. Our objective was to raise awareness around the unpredictable association between COVID-19 pandemics and the upcoming flu season, and make arguments about the need to develop new routine testing protocols for both viruses, at least during the period with an expected high incidence. Our reasoning is built around the various impacts that the whole range of risk groups, common immunological mechanisms, and complex interactions, such as influenza vaccination, will have on patients' prognosis. We show that the more flawed clinical course is due to managing only one of the infections (and, subsequently, neglecting the other condition).

A Machine Learning Model Accurately Predicts Ulcerative Colitis Activity at One Year in Patients Treated with Anti-Tumour Necrosis Factor α Agents.

Background and objectives: The biological treatment is a promising therapeutic option for ulcerative colitis (UC) patients, being able to induce subclinical and long-term remission. However, the relatively high costs and the potential toxicity have led to intense debates over the most appropriate criteria for starting, stopping, and managing biologics in UC. Our aim was to build a machine learning (ML) model for predicting disease activity at one year in UC patients treated with anti-Tumour necrosis factor α agents as a useful tool to assist the clinician in the therapeutic decisions. Materials and Methods: Clinical and biological parameters and the endoscopic Mayo score were collected from 55 UC patients at the baseline and one year follow-up. A neural network model was built using the baseline endoscopic activity and four selected variables as inputs to predict whether a UC patient will have an active or inactive endoscopic disease at one year, under the same therapeutic regimen. Results: The classifier achieved an excellent performance predicting the disease activity at one year with an accuracy of 90% and area under curve (AUC) of 0.92 on the test set and an accuracy of 100% and an AUC of 1 on the validation set. Conclusions: Our proposed ML solution may prove to be a useful tool in assisting the clinicians' decisions to increase the dose or switch to other biologic agents after the model's validation on independent, external cohorts of patients.

Enhancing Calprotectin's Predictive Power as a Biomarker of Endoscopic Activity in Ulcerative Colitis: A Machine Learning Use Case.

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.

Laboratory Data and IBDQ-Effective Predictors for the Non-Invasive Machine-Learning-Based Prediction of Endoscopic Activity in Ulcerative Colitis.

A suitable, non-invasive biomarker for assessing endoscopic disease activity (EDA) in ulcerative colitis (UC) has yet to be identified. Our study aimed to develop a cost-effective and non-invasive machine learning (ML) method that utilizes the cost-free Inflammatory Bowel Disease Questionnaire (IBDQ) score and low-cost biological predictors to estimate EDA. Four random forest (RF) and four multilayer perceptron (MLP) classifiers were proposed. The results show that the inclusion of IBDQ in the list of predictors that were fed to the models improved accuracy and the AUC for both the RF and the MLP algorithms. Moreover, the RF technique performed noticeably better than the MLP method on unseen data (the independent patient cohort). This is the first study to propose the use of IBDQ as a predictor in an ML model to estimate UC EDA. The deployment of this ML model can furnish doctors and patients with valuable insights into EDA, a highly beneficial resource for individuals with UC who need long-term treatment.

Survival Prediction in Diabetic Foot Ulcers: A Machine Learning Approach.

Our paper proposes the first machine learning model to predict long-term mortality in patients with diabetic foot ulcers (DFUs). The study includes 635 patients with DFUs admitted from January 2007 to December 2017, with a follow-up period extending until December 2020. Two multilayer perceptron (MLP) classifiers were developed. The first MLP model was developed to predict whether the patient will die in the next 5 years after the current hospitalization. The second MLP classifier was built to estimate whether the patient will die in the following 10 years. The 5-year and 10-year mortality models were based on the following predictors: age; the University of Texas Staging System for Diabetic Foot Ulcers score; the Wagner-Meggitt classification; the Saint Elian Wound Score System; glomerular filtration rate; topographic aspects and the depth of the lesion; and the presence of foot ischemia, cardiovascular disease, diabetic nephropathy, and hypertension. The accuracy for the 5-year and 10-year models was 0.7717 and 0.7598, respectively (for the training set) and 0.7244 and 0.7087, respectively (for the test set). Our findings indicate that it is possible to predict with good accuracy the risk of death in patients with DFUs using non-invasive and low-cost predictors.

The Influence of Metabolic Factors in Patients with Chronic Viral Hepatitis C Who Received Oral Antiviral Treatment.

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.

Evaluation of Disease Activity in Inflammatory Bowel Disease: Diagnostic Tools in the Assessment of Histological Healing.

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 Prognostic Performance of Ferritin in Patients with Acute Myocardial Infarction: A Systematic Review.

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.

"Not by Our Feeling, But by Other's Seeing": Sentiment Analysis Technique in Cardiology-An Exploratory Review.

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.

Magnesium-A Potential Key Player in Inflammatory Bowel Diseases?

The altered magnesium status in inflammatory bowel disease (IBD) patients may have a significant clinical imprint considering its role in cell signaling and genomic stability, as well as its involvement in IBD patients' fatigue. Our study pioneers the investigation of magnesium hair concentration patterns in an adult population of IBD patients. The hair magnesium concentration in IBD patients is compared to healthy controls in order to identify correlations between the magnesium status and relevant parameters related to disease activity, psychological status, and sleep quality. We report a significantly lower hair magnesium concentration within the IBD group compared to healthy controls (95%CI: 0.006-0.062; p = 0.017) and lower levels in CD compared to UC (95%CI: -0.061--0.002; p = 0.038). We identified a borderline statistical significance between the hair magnesium concentration and UC disease activity (95%CI; -0.679-0.008; p = 0.055) and a significantly lower magnesium concentration in patients who reported increased sleep latency (95%CI -0.65--0.102; p = 0.011) or decreased sleep duration (95%CI -0.613--0.041; p = 0.028). Our results advance several hypotheses with substantial clinical impact to be confirmed in future studies. Magnesium levels appear to be modified in IBD patients, which suggests it either plays a primary role in disease pathophysiology or a is result of the disease's evolution. Magnesium could be used in predictive models for clinical/subclinical disease activity. Moreover, magnesium supplementation may improve IBD evolution and sleep quality for patients with a deficit of this mineral. However, confirmatory evidence-based studies are needed to generate specific dosing, time of supplementation, and optimum monitoring of magnesium status in IBD patients.

Determinants of the Hesitancy toward COVID-19 Vaccination in Eastern European Countries and the Relationship with Health and Vaccine Literacy: A Literature Review.

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.