Oana Roxana Ciobotaru

Prolonged hospitalization in cardiac patients is associated with increased morbidity and healthcare resource utilization, yet early biological factors linked to extended length of stay remain insufficiently defined. This study aimed to explore an integrative framework combining oral health parameters and targeted gut microbial markers to identify factors associated with prolonged hospitalization in cardiac patients. A comparative observational design was applied, including patients with short-term hospitalization (1–4 days) and prolonged hospitalization (≥25 days). Oral health status was evaluated using a standardized dental protocol at admission and longitudinally in patients with prolonged hospitalization. Targeted qRT-PCR-based quantification of selected gut bacterial markers was performed at admission and reassessed after one and two weeks. Temporal changes were calculated relative to baseline, and multivariate logistic regression models adjusted for age, sex, and major cardiac diagnoses were used to explore associations with prolonged hospitalization. Short-term hospitalized patients (n = 27) exhibited minimal oral health variation (+2%) and stable marker profiles. In contrast, patients with prolonged hospitalization (n = 30 for oral health; n = 18 for microbial markers) showed progressive changes over time. Oral health impairment increased by 3% after one week and 16% after two weeks, while targeted microbial marker variation showed modest directional changes. Integrative models combining oral health parameters and targeted microbial markers suggested potential complementary information alongside clinical variables, within the limits of an exploratory framework and limited sample size. These findings support the relevance of multidomain clinical and biological monitoring in the early identification of patients at risk for prolonged hospitalization.

Background/Objectives: Long-term hospitalization in cardiac patients is associated with significant metabolic and microbial alterations that may influence disease progression and prognosis. Although lipid imbalances, metabolomic shifts, and gut microbiome dysbiosis have each been linked individually to cardiovascular outcomes, their integrated evaluation in long-term-hospitalized patients remains underexplored. Methods: We conducted a retrospective observational study including 51 cardiac patients hospitalized for more than 25 days, compared with a control group of 41 patients hospitalized for short and intermediate durations (3–24 days). Clinical and demographic data were collected, alongside lipid profiling, metabolomic assessment through liquid chromatography–mass spectrometry (LC-MS), and gut microbiome analysis using GI360™ sequencing. Ethical approval was obtained, and all data were anonymized. Lipid-related findings are exploratory due to the small number of complete measurements. Results: Preliminary lipid trends were characterized by higher levels of LDL, triglycerides, and Lp(a), and lower HDL, in the long-term group. Metabolomic analyses revealed decreased energy-related metabolites (ATP, phosphocreatine ratio), altered amino acid patterns, and increased ketone utilization. Gut microbiome evaluation demonstrated a significant increase in dysbiosis index, with reduced diversity and dominance of potentially pathogenic taxa. These findings were correlated with clinical severity scores. Cross-domain relationships are exploratory and based on associative profiling rather than deep integrative modelling. Conclusions: Long-term hospitalization in cardiac patients is associated with distinct lipid, metabolomic, and gut microbiome profiles that may serve as predictive biomarkers of adverse outcomes. Future studies should validate these findings in larger cohorts and explore their integration into personalized management strategies.

Burnout in healthcare professionals is a critical occupational health concern with significant psychiatric implications. It is strongly linked to higher risks of depression, anxiety, PTSD, and suicidality. This systematic review synthesises existing literature on the prevalence, risk factors, mental health consequences, and psychotherapeutic interventions for burnout in healthcare professionals, integrating findings from seven included studies. A systematic literature search was conducted across three major databases (PubMed, Web of Science, and Google Scholar) in accordance with PRISMA 2020 standardised guidelines. Eligible studies included systematic reviews, meta-analyses, and observational studies (cross-sectional, group, and case-control) published within the last five years. The initial search yielded 5,000 articles, of which 1,200 duplicates were removed. After title, abstract and content screening, 100 full-text articles were assessed for eligibility. Ultimately, seven studies met the inclusion criteria and were included in this review. The burnout prevalence varied significantly across studies, with reported rates ranging from 30% to 75%, particularly among physicians, nurses, and emergency healthcare workers. The most frequently identified risk factors included high workload, emotional exhaustion, limited autonomy, and inadequate institutional support. Notably, burnout was found to correlate with psychiatric symptoms, including increased rates of major depressive disorder, generalised anxiety disorder, PTSD, and suicidal ideation. Neurobiological findings suggest that burnout shares common pathways with stress-related psychiatric conditions, including dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis and altered connectivity in brain regions associated with emotional regulation. The consequences of burnout extended beyond mental health deterioration to include cognitive impairment, impaired clinical decision-making, and higher medical error rates, posing risks to both healthcare providers and patients. Despite the severity of these outcomes, several interventions demonstrated effectiveness in mitigating burnout and its psychiatric sequelae. These included structured cognitive-behavioural therapy (CBT), mindfulness-based stress reduction (MBSR), peer support networks, and resilience training programmes. Institutional strategies, such as workload redistribution and mental health screening, were also found to be beneficial in preventing the escalation of burnout into more severe psychiatric disorders. The burnout syndrome represents a significant psychiatric and occupational health crisis in healthcare, necessitating targeted interventions at both individual and systemic levels. The integration of mental health screening protocols, early psychiatric intervention, and structured psychological support, within healthcare settings can play a pivotal role in preventing burnout-related mental health disorders. Future research should prioritise longitudinal studies and multimodal approaches—incorporating neuroimaging, biomarker analysis, and real-time monitoring—to better understand the psychiatric dimensions of burnout and develop sustainable intervention frameworks.

Cardiovascular diseases (CVDs) are the leading global cause of death, with long-term hospitalization becoming increasingly frequent in advanced or chronic cases. In this context, the interplay between systemic factors such as lipid metabolism, circulating metabolites, gut microbiota, and oral health is gaining attention for its potential role in influencing inflammation, cardiometabolic risk, and long-term outcomes. Despite their apparent independence, these domains are increasingly recognized as interconnected and influential in cardiovascular pathophysiology. Methods: This narrative review was conducted by analyzing studies published between 2015 and 2024 from databases including PubMed, Scopus, and Web of Science. Keywords such as “lipid profile,” “metabolomics,” “gut microbiota,” “oral health,” and “cardiovascular disease” were used. Original research, meta-analyses, and reviews relevant to hospitalized cardiac patients were included. A critical integrative approach was applied to highlight cross-domain connections. Results and Discussion: Evidence reveals significant interrelations between altered lipid profiles, gut dysbiosis (including increased TMAO levels), metabolic imbalances, and oral inflammation. Each component contributes to a systemic pro-inflammatory state that worsens cardiovascular prognosis, particularly in long-term hospitalized patients. Despite isolated research in each domain, there is a paucity of studies integrating all four. The need for interdisciplinary diagnostic models and preventive strategies is emphasized, especially in populations with frailty or immobilization. Conclusions: Monitoring lipid metabolism, metabolomic shifts, gut microbial balance, and oral status should be considered part of comprehensive cardiovascular care. Gut microbiota exerts a dual role in cardiac health: when balanced, it supports anti-inflammatory and metabolic homeostasis; when dysbiotic, it contributes to systemic inflammation and worsened cardiac outcomes. Future research should aim to develop integrative screening tools and personalized interventions that address the multifactorial burden of disease. A systemic approach may improve both short- and long-term outcomes in this complex and vulnerable patient population.

Background: Cardiogenic shock (CS) is a life-threatening complication of ST-elevation myocardial infarction (STEMI) and remains the leading cause of in-hospital mortality, with rates ranging from 5 to 10% despite advances in reperfusion strategies. Early identification and timely intervention are critical for improving outcomes. This study investigates the utility of machine learning (ML) models for predicting the risk of CS during the early phases of care—prehospital, emergency department (ED), and cardiology-on-call—with a focus on accurate triage and prioritization for urgent angiography. Results: In the prehospital phase, the Extra Trees classifier demonstrated the highest overall performance. It achieved an accuracy (ACC) of 0.9062, precision of 0.9078, recall of 0.9062, F1-score of 0.9061, and Matthews correlation coefficient (MCC) of 0.8140, indicating both high predictive power and strong generalization. In the ED phase, the support vector machine model outperformed others with an ACC of 78.12%. During the cardiology-on-call phase, Random Forest showed the best performance with an ACC of 81.25% and consistent values across other metrics. Quadratic discriminant analysis showed consistent and generalizable performance across all early care stages. Key predictive features included the Killip class, ECG rhythm, creatinine, potassium, and markers of renal dysfunction—parameters readily available in routine emergency settings. The greatest clinical utility was observed in prehospital and ED phases, where ML models could support the early identification of critically ill patients and could prioritize coronary catheterization, especially important for centers with limited capacity for angiography. Conclusions: Machine learning-based predictive models offer a valuable tool for early risk stratification in STEMI patients at risk for cardiogenic shock. These findings support the implementation of ML-driven tools in early STEMI care pathways, potentially improving survival through faster and more accurate decision-making, especially in time-sensitive clinical environments.

Background: While early risk stratification in STEMI is essential, the threat of cardiogenic shock (CS) persists after revascularization due to reperfusion injury and evolving instability. However, risk prediction in later phases—after revascularization—is less explored, despite its importance in guiding intensive care decisions. This study evaluates machine learning (ML) models for dynamic risk assessment in interventional cardiology and cardiac intensive care unit (CICU) phases, where timely detection of deterioration can guide treatment escalation. Methods: We retrospectively analyzed clinical and procedural data from 158 patients diagnosed with STEMI complicated by cardiogenic shock, treated between 2019 and 2022 at the Cardiology Department of the University Emergency Hospital of Bucharest, Romania. Machine learning models—Random Forest (RF), and Quadratic Discriminant Analysis (QDA)—were developed and tested specifically for the interventional cardiology and CICU phases. Model performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), accuracy (ACC), sensitivity, specificity, and F1-score. Results: In the interventional phase, RF and QDA achieved the highest accuracy, both reaching 87.50%. In the CICU, RF and QDA demonstrate the best performance, reaching ACCs of 0.843. QDA maintained consistent performance across phases. Relevant predictors included reperfusion strategy, TIMI flow before percutaneous coronary intervention (PCI), Killip class, creatinine, and Creatine Kinase Index (CKI)—all parameters routinely assessed in STEMI patients. These models effectively identified patients at risk for post-reperfusion complications and hemodynamic decline, supporting decisions regarding extended monitoring and ICU-level care. Conclusions: Predictive models implemented in advanced STEMI phases can contribute to dynamic, phase-specific risk reassessment and optimize CICU resource allocation. These findings support the integration of ML-based tools into post-PCI workflows, enabling earlier detection of clinical decline and more efficient deployment of intensive care resources. When combined with earlier-stage models, the inclusion of interventional and CICU phases forms a dynamic, end-to-end risk assessment framework. With further refinement, this system could be implemented as a mobile application to support clinical decisions throughout the STEMI care continuum.

Echinococcosis represents a major public health issue and continues to be endemic in various regions around the world. Hydatid cysts are commonly located in the liver (primary site), followed by the lungs (secondary site). However, they are also found in less typical organs such as the spleen, kidneys, peritoneum, retroperitoneum, pancreas, gallbladder, and various other organs, as well as in striate muscles. Hydatid cysts involving the psoas muscle are rare, and in the past five years, only a few articles have addressed this condition due to its low incidence. Symptoms can be quite vague, and serological testing might return negative, making imaging studies crucial for an accurate diagnosis. Treatment options involve surgery alongside antiparasitic agents. Despite having a low mortality rate, the recurrence of hydatid disease remains high. This paper aims to provide new clinical data through our case presentation, while also offering a review of the cases of hydatid cysts involving the psoas muscle that have been published over the past five years.

Aim of the study: Adenocarcinoma of the gallbladder diagnosed incidentally can reach up to 50% of gallbladder cancer cases, but with a life expectancy double that of those diagnosed non-incidentally. Gallstones represent a risk factor for this pathology. Material and method. We conducted a retrospective study, between 2011-2016, on a number of 341 patients, over 18 years old, cholecystectomized for bladder lithiasis. Acute cholecystitis, patients with acute or chronic inflammatory diseases, open surgical interventions, choledocholithiasis, cholelithiasis and associated liver pathology were excluded. Results: The histopathological examination identified 8 cases of adenocarcinoma, 2 well differentiated, 6 moderately differentiated. Adenocarcinoma represented 2.35% of the total cases studied. The average age of appearance of well-differentiated adenocarcinoma was lower than that of moderately differentiated (50.00 ± 2.828 vs 58.00 ± 3.347). The most frequent cases were in female patients (87.5%). Urban origin was the most common, only 12.5% of patients with gallbladder neoplasms came from rural areas. Conclusions: All patients with gallstones should undergo cholecystectomy followed by histopathological examination in order to identify an early adenocarcinoma of the gallbladder.

Background and Objective: New-onset atrial fibrillation (NOAF) is a common cardiac condition often observed in intensive care units. When amiodarone is used to treat this condition, either to maintain sinus rhythm after electrical cardioversion or to control heart rate, complications can arise when a systemic pathology is present. Systemic pathology can result in a decrease in cardiac output and blood pressure, making the management of NOAF and septic shock challenging. Limited international research exists on the coexistence of NOAF and septic shock, making it difficult to determine the optimal course of treatment. While amiodarone is not the primary choice of antiarrhythmic drug for patients in septic shock, it may be considered for those with underlying cardiac issues. This paper aims to investigate the safety of administering amiodarone to patients with septic shock and explore whether another antiarrhythmic drug may be more effective, especially considering the cardiac conditions that patients may have. Materials and Methods: To write this article, we searched electronic databases for studies where authors used amiodarone and other medications for heart rate control or sinus rhythm restoration. Results: The studies reviewed in this work have shown that for the patients with septic shock and NOAF along with a pre-existing cardiac condition like a dilated left atrium, the use of amiodarone may provide greater benefits compared to other antiarrhythmic drugs. For patients with NOAF and septic shock without underlying heart disease, the initial use of propafenone has been found to be advantageous. However, a challenge arises when deciding between rhythm or heart rate control using various drug classes. Unfortunately, there is limited literature available on this specific scenario. Conclusions: NOAF is a frequent and potentially life-threatening complication occurring in one out of seven patients with sepsis, and its incidence is rising among patients with septic shock.

Laparoscopic cholecystectomy is characterized by reduced postoperative pain, shorter hospital stays, rapid return to preoperative physical activity, and less psychological impact on the patient. During laparoscopic cholecystectomy, the intra-abdominal insufflation of carbon dioxide with secondary increase in intra-abdominal pressure can cause important hemodynamic consequences, like decreased cardiac output and blood pressure, as well as compensatory increase in heart rate. The purpose of this study is to evaluate changes in cardiovascular parameters during general anesthesia in patients undergoing laparoscopic cholecystectomy. Retrospective data from 342 patients with cholecystectomy for cholelithiasis performed at Railway Hospital Galati, Romania, were reviewed. All patients received the same intraoperative anesthetics. Female patients were 85.7% (n = 293). More than half of the patients, 53.51% (n = 183), were 40–59 years old, and only 16.37% (n = 56) were under 40 years old. Patients with a normal body mass index (BMI) represented 45.6% (n = 156), 33.3% (n = 114) were underweight, and 12% (n = 42) had grade 1 obesity (BMI 25–29.9 kg/m²). The minimum intraoperative blood pressure correlated with patient gender (p 0.015 < 0.005), with men having a higher blood pressure than women (p 0.006 < 0.05), and for BMI, a higher BMI was associated with elevated blood pressure (p 0.025 < 0.05). Older age correlated with an increased maximum intraoperative blood pressure (p < 0.001 < 0.05) and with maximum intraoperative heart rate (p 0.015 < 0.05). Patients undergoing laparoscopic cholecystectomy experienced significant hemodynamic changes with pneumoperitoneum, but this type of surgical intervention was safe for patients regardless of their age.