Aim: To investigate the dynamics of energy expenditure in patients with severe traumatic brain injury (TBI) during an intensive care unit (ICU) stay and to assess the effects of infection, sedation, sedation depth, and neurological status. Materials and methods: This prospective observational study was conducted in the ICU of St. George University Hospital in Plovdiv over 18 months. Thirty patients with severe TBI were included. Measured resting energy expenditure (mREE) was assessed using indirect calorimetry (Q-NRG+, Cosmed). Metabolic status was defined using the energy metabolic index, expressed as a percentage of Harris–Benedict predicted energy expenditure. Results: The measured REE increased progressively during ICU stay, reaching peak values between days 11 and 20 (31.08±4.61 kcal/kg/day; 115±15%), followed by a moderate decline while remaining above predicted levels. Marked interindividual variability was observed, with hypo-, normo-, and hypermetabolic states throughout all periods. Energy expenditure was significantly higher during infection compared to non-infectious periods (111±23% vs. 103±21%, p=0.037). Sedation was associated with lower energy expenditure (103±20% vs. 111±25%, p=0.041), with a significant inverse relationship between sedation depth and metabolic rate (p<0.001). Neurological status and inflammatory markers showed no significant association with metabolic status. Conclusions: Energy expenditure in severe TBI is dynamic and highly variable during an ICU stay. Infection increases metabolic demand, whereas sedation reduces metabolic rate in a dose-dependent manner. These findings support individualized metabolic monitoring to optimize nutritional management.
Introduction: Bulgaria stands out in Eastern Europe for its early and structured engagement in rare disease policymaking through the National Program for Rare Diseases (2009–2013) and Ordinance No. 16/2014, which established the legal framework for rare disease registration and management. Aim: To present a retrospective policy implementation analysis of Bulgaria’s rare disease framework, examining how the provisions introduced by Ordinance No. 16/2014 have been translated into measurable institutional outputs between July 2014 and March 2025. Materials and methods: A retrospective policy implementation analysis was conducted using aggregated administrative and regulatory data derived from publicly accessible sources, official documents, and materials obtained through formal requests under the Bulgarian Law on Access to Public Information. Results: The national List of Rare Diseases, adopted in 2015, initially included 116 conditions. Between 2015 and 2025, 149 additional conditions were added following expert review. Median processing times were 112 days from initial review to Commission recommendation and 126 days from recommendation to ministerial approval. During the same period, 34 expert centers were designated, with a median of 139 days from submission to certification. By March 2025, the National Registry contained data on 1685 patients across 76 rare diseases; however, reporting remained incomplete, with only 35.3% of expert centers contributing data. Conclusions: The Bulgarian model demonstrates that a permanent regulatory framework can improve coordination, visibility, and standardization of rare disease care. Nevertheless, incomplete registry reporting, uneven stakeholder participation, and limited integration within European Reference Networks remain important challenges.
Lung cancer remains the leading cause of cancer-related mortality worldwide, with delayed diagnosis being a major contributor to poor outcomes. Artificial intelligence (AI) has emerged as a transformative tool for enhancing diagnostic accuracy, improving early detection, and enabling personalized treatment strategies. This non-systematic review evaluates current advancements in AI for lung cancer diagnosis across imaging, pathology, and integrative risk stratification. AI-assisted radiological tools, including deep learning models applied to chest X-rays, CT scans, PET/CT, and thoracoscopic images, have demonstrated superior sensitivity and specificity in detecting pulmonary nodules and staging disease compared to conventional radiology. In pathology, AI applications in histological classification, biomarker detection, and prediction of tumor invasiveness have significantly improved diagnostic precision and reproducibility, supporting tailored therapeutic decision-making. Integrative multimodal AI models that combine imaging, clinical, and genomic data have further enhanced prognostic assessment and risk stratification. Despite these advancements, challenges remain regarding data availability, interpretability, ethical concerns, and legal implications of AI autonomy in clinical practice. Future progress will require explainable, transparent, and prospectively validated AI systems developed in close collaboration between clinicians and technology experts. Overall, AI holds the potential to revolutionize lung cancer diagnostics by enabling earlier detection, personalized care, and improved patient outcomes.
Introduction: Endometritis is an inflammation of the endometrium that might be associated with, or present as, some gynecological clinical conditions. However, there is a lack of studies exploring the condition in our locality. Aim: This study aimed to assess the cases of endometritis diagnosed over a period of six years with insight into the relation to different clinico-pathological parameters. Material and methods: A retrospective cross-sectional study was conducted on 137 female patients diagnosed with endometritis. Data were retrieved from hospital records, including demographic, clinical, histopathological, microbiological, and laboratory findings. Statistical analyses were performed using SPSS version 25, including descriptive statistics, chi-square tests, and logistic regression. Results: Among the 137 patients, most were aged between 30 and 50 years. Chronic endometritis constituted about 90% of cases. Abnormal uterine bleeding and lower abdominal pain were the most common symptoms. Among the positive cultures, Gram-positive bacteria were the most identified pathogens. About 3% of women presenting to the general gynecology clinic were diagnosed with endometritis, highlighting a notable prevalence within the population. Conclusions: Endometritis predominantly affects women of reproductive age and is mainly chronic. Clinical evaluation supported by histopathological confirmation and integration of sensitive laboratory markers is essential for proper diagnosis and management.
Bachground: Peptic ulcer disease refers to deep erosions in the mucosal layer of the gastrointestinal tract (GIT) and is commonly associated with psychological disorders such as depression. Depression is characterized by feelings of hopelessness, insomnia, and guilt. Gastrin-17 is an important hormone that regulates the gastrointestinal system and controls acid secretion and gastric cell proliferation. Aim: To estimate the serum level of gastrin-17 and assess its potential as a diagnostic biomarker for peptic ulcer disease related to depression. Materials and methods: A case-control study was conducted involving 50 males diagnosed with peptic ulcer by endoscopy and 50 healthy males as controls. Gastrin-17 and vitamin D3 levels were measured using ELISA. The Mann–Whitney U test was used for comparison, and the sensitivity, specificity, and accuracy of gastrin-17 was assessed using the ROC curve test. Results: Gastrin-17 levels differed significantly between peptic ulcer patients and controls (p<0.01), higher in patients (188.95 [131.99 - 227.11]) than in controls (70.94 [57.025 -79.31]). There was a significant decrease (p<0.01) in serum vitamin D3 in peptic ulcer patients (82.93 [44.896 - 91.211]) than in controls (173.19 [164.51 - 181.786]). The ROC curve for gastrin-17 showed AUC 0.999, with 98% sensitivity, 100% specificity, and 99% accuracy. Conclusion: This study found significant differences in gastrin-17 levels between males with peptic ulcer disease and controls. It discussed reasons for elevated levels in PUD complicated by depression and the bidirectional relationship between these two conditions, highlighting its potential as a diagnostic tool, as shown by ROC curve analysist.
