Background: The aim of this review is to explore the role of artificial intelligence in the diagnosis of colorectal cancer, how it impacts CRC morbidity and mortality, and why its role in clinical medicine is limited. Methods: A targeted, non-systematic review of the published literature relating to colorectal cancer diagnosis was performed with PubMed databases that were scouted to help provide a more defined understanding of the recent advances regarding artificial intelligence and their impact on colorectal-related morbidity and mortality. Articles were included if deemed relevant and including information associated with the keywords. Results: The advancements in artificial intelligence have been significant in facilitating an earlier diagnosis of CRC. In this review, we focused on evaluating genomic biomarkers, the integration of instruments with artificial intelligence, MR and hyperspectral imaging, and the architecture of neural networks. We found that these neural networks seem practical and yield positive results in initial testing. Furthermore, we explored the use of deep-learning-based majority voting methods, such as bag of words and PAHLI, in improving diagnostic accuracy in colorectal cancer detection. Alongside this, the autonomous and expansive learning ability of artificial intelligence, coupled with its ability to extract increasingly complex features from images or videos without human reliance, highlight its impact in the diagnostic sector. Despite this, as most of the research involves a small sample of patients, a diversification of patient data is needed to enhance cohort stratification for a more sensitive and specific neural model. We also examined the successful application of artificial intelligence in predicting microsatellite instability, showcasing its potential in stratifying patients for targeted therapies. Conclusions: Since its commencement in colorectal cancer, artificial intelligence has revealed a multitude of functionalities and augmentations in the diagnostic sector of CRC. Given its early implementation, its clinical application remains a fair way away, but with steady research dedicated to improving neural architecture and expanding its applicational range, there is hope that these advanced neural software could directly impact the early diagnosis of CRC. The true promise of artificial intelligence, extending beyond the medical sector, lies in its potential to significantly influence the future landscape of CRC’s morbidity and mortality.

Simulation training in medicine is a powerful tool for acquiring knowledge and practical skills in an environment close to the real world. Decreasing training opportunities directly on the patient tend to introduce simulations close to the real hospital environment without compromising the patient’s safety. The aim of the study was to evaluate the effectiveness of simulation training in the Medical Simulation Training Center (MSTC) at the Medical University of Plovdiv. A prospective observational descriptive study was performed at the MSTC at Plovdiv Medical University between September 2017 and March 2019. The study included 245 respondents who participated in one or more practical simulation trainings and were surveyed. The results of the study suggest that the knowledge, skills and attitudes of the trainees improve after training sessions. By increasing the number of visits, the self-esteem and confidence in performing the manipulation also increases, knowledge and technical performance significantly improve and mistakes are reduced. A statistically significant relationship has been demonstrated between the sequence of visits and acquired competencies (p=0.0001). There are no performance mistakes in those attending three or more times, but 16.2% of them still experience uncertainty while performing. Here, however, 83.8% performed the manipulation automatically and without mistakes. Simulation training in medicine is effective and useful. It has its place in the curriculum for students and is a good tool for acquiring knowledge, skills and techniques in postgraduate education.

This comprehensive review delves into the complex realm of antinuclear antibodies (ANAs), expanding beyond their traditional involvement in autoimmune rheumatic disorders. By digging into historical changes, diagnostic complexity, and clinical significance, the debate reveals the shifting relationships between ANAs, particularly with cancer. Specialized studies provide practical insights on ANA testing processes, standardization, and upcoming challenges. Examining prevalence trends in the United States provides a time dimension to ANA dynamics, linking autoimmune and oncological considerations. The debate delves into the complexity of lupus erythematosus, emphasizing ANAs’ diverse presentations and their potential as flexible diagnostic and prognostic indicators. The complex relationship between ANAs and cancer is highlighted, demonstrating their potential as early markers or indicators of malignancies. Looking ahead, this synthesis anticipates advances in personalized medicine and collaborative research, putting ANAs at the forefront of advanced diagnostics and treatments for autoimmune disorders and cancer. This synthesis envisions a future for ANA research in which these antibodies play a critical role in promoting personalized treatment, enhancing diagnostics, and fostering collaborative initiatives that cross traditional boundaries. As ANAs grow more prominent at the junction of autoimmune illnesses and cancer, this synthesis lays the path for further research and novel advances in understanding, diagnosing, and treating complicated medical conditions.

Background and objectives: This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. Method: A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. Results: Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. Conclusions: When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine.

Background/Objectives: Maternal overweight and obesity are critical factors increasing the risk of various pregnancy complications. Maternal obesity can lead to fetal macrosomia and a heightened risk of intrauterine death, with long-term implications for the child’s health. This study aimed to analyze the incidence of obesity and its impact on pregnancy outcomes in women who delivered by cesarean section at the University Hospital “St. George”, Plovdiv. Methods: A single-center retrospective cohort study was conducted. The documentary method was used for gathering data. Records were randomly selected. The statistical methods used included mean values, confidence intervals (of mean), frequency, and the Kolmogorov–Smirnov test for normality of distribution. Data comparisons were performed using the Mann–Whitney test. Mean values of numerical variables were compared using the independent samples t-test. Results: In total, 46.36% of women in this study were affected by obesity to varying degrees, and the proportion of women who were overweight at the end of their pregnancy was 37.85%. In the studied cohort, 15.99% of women were affected by hypertensive complications. This significant prevalence of obesity highlights concerns regarding body weight among women of reproductive age. This study emphasized a strong correlation between maternal obesity, particularly severe obesity, and the occurrence of preeclampsia. Conclusions: In this study among women who delivered by cesarean section, a significant proportion of them were affected by overweight and obesity. Data for our country are insufficient, and a more in-depth study of this problem is needed. Future research should explore the long-term impacts of maternal obesity on the health of the mother and the newborn.

Introduction: Hydatidosis is one of the most critical parasitic zoonotic diseases worldwide. Lungs are the second most common site of the disease. Aim: This study aimed to evaluate the clinical presentation, diagnosis, radiological findings, surgical treatment and post-operative complications of pulmonary hydatid cyst in patients referred to the Second Surgery Clinic at St George University Hospital in Plovdiv, Bulgaria. Materials and methods: From January 2014 to December 2022, 69 patients were operated on due to pulmonary hydatidosis in the Clinic of Thoracic and Abdominal Surgery at St George University Hospital in Plovdiv, Bulgaria. Demographic data of the patients were collected including clinical symptoms, laboratory studies, radiological findings, location of the lung involvement, and surgical procedures and post-operative complications were recorded. Results: Pulmonary hydatidosis was confirmed for 69 (46 male and 23 female) patients. The average age of the study group was 31.59±19.49 years. In 29 of the tested patients, specific antibodies of the IgG class to Echinococcus granulosus were positive, and 23 patients showed negative results. The leading symptom was non-productive cough followed by shortness of breath, chest pain, fever, productive cough; rash was reported in one patient. 91.3% of the cases presented with a typical X-ray image and in 8.7% it was atypical. It turned out that 66.7% of patients with an atypical X-ray findings developed complications in the postoperative period. Right lung was affected in 50.7% of the cases. The average size of the echinococcal cyst was 7.43±3.23 cm. Statistical analysis revealed a correlation between the size of the hydatid cyst and the length of the postoperative period. Dissemination in other organs was found in 16 of the patients. No statistically significant differences were reported in the patients with dissemination, regarding the size of the primary hydatid cyst, complications and duration of the postoperative period. The preferred methods of approach were thoracotomy and VATS. The most commonly performed surgical technique was echinococcectomy and capitonnage a modo Delbet followed by two lobectomies and one wedge resection. The mean postoperative period was 7.00±2.81 days. In 12 of the analyzed patients, complications occurred in the postoperative period, and mortality was reported in one patient. Conclusion: Pulmonary echinococcosis remains a significant health issue in many countries worldwide including Bulgaria. With a prolonged silent period, it is usually diagnosed when clinical symptoms as cough, dyspnea, chest pain and fever start manifesting. Plain chest radiography remains the basic imaging method for initial diagnosis while ELISA test has limited diagnostic accuracy in cases of pulmonary hydatidosis. Irrelevant to surgical approach, Delbet’s capitonnage procedure is associated with good postoperative results and low complication rates.