AIM To evaluate the clinical impact of corticosteroids (CS) overuse in inflammatory bowel disease (IBD) patients. Excessive use of CS could delay more efficacious treatment and may indicate poor quality of care. METHOD This is a two-phase study that used Steroid Assessment Tool (SAT) to measure corticosteroid exposure in IBD patients. In the first phase, data from 211 consecutive ambulatory patients with IBD (91 with ulcerative colitis, 115 with Crohn's disease, and five with unclassified inflammatory bowel disease) were analysed by SAT. In the second phase, one year after data entry, clinical outcome of patients with corticosteroids overuse was analysed. RESULTS Of the 211 IBD patients, 132 (62%) were not on corticosteroids, 45 (22%) were corticosteroid-dependent, and 34 (16%) used corticosteroids appropriately, according to the European Crohn's and Colitis Organization guidelines. In the group of patients with ulcerative colitis, 57 (63%) were not on corticosteroids, 18 (20%) were corticosteroid-dependent, and 16 (16%) used corticosteroids appropriately; in the group of patients with Crohn's disease 70 (61%), 27 (23%) and 18 (16%), respectively. Overall, 24 (out of 45; 53%) patients with IBD could avoid the overuse of corticosteroids if they had a timely change of the treatment, surgery, or entered a clinical trial. CONCLUSION An excessive corticosteroid use can be recognized on time using the SAT. We have proven that excessive corticosteroid use could be avoided in almost half of cases and thus the overuse of CS may indicate poor quality of care in those patients.

Prediction of short-term mortality in patients with acute decompensation of liver cirrhosis could be improved. We aimed to develop and validate two machine learning (ML) models for predicting 28-day and 90-day mortality in patients hospitalized with acute decompensated liver cirrhosis. We trained two artificial neural network (ANN)-based ML models using a training sample of 165 out of 290 (56.9%) patients, and then tested their predictive performance against Model of End-stage Liver Disease-Sodium (MELD-Na) and MELD 3.0 scores using a different validation sample of 125 out of 290 (43.1%) patients. The area under the ROC curve (AUC) for predicting 28-day mortality for the ML model was 0.811 (95%CI: 0.714- 0.907; p < 0.001), while the AUC for the MELD-Na score was 0.577 (95%CI: 0.435–0.720; p = 0.226) and for MELD 3.0 was 0.600 (95%CI: 0.462–0.739; p = 0.117). The area under the ROC curve (AUC) for predicting 90-day mortality for the ML model was 0.839 (95%CI: 0.776- 0.884; p < 0.001), while the AUC for the MELD-Na score was 0.682 (95%CI: 0.575–0.790; p = 0.002) and for MELD 3.0 was 0.703 (95%CI: 0.590–0.816; p < 0.001). Our study demonstrates that ML-based models for predicting short-term mortality in patients with acute decompensation of liver cirrhosis perform significantly better than MELD-Na and MELD 3.0 scores in a validation cohort.

Inflammatory bowel disease (IBD), encompassing Crohn’s disease (CD) and ulcerative colitis (UC), necessitates effective management strategies. This study aims to evaluate the real-world efficacy of vedolizumab, a newer biological therapy, in treating IBD in Bosnia and Herzegovina. A retrospective observational study was conducted across six medical centers, involving 139 IBD patients, 76 with UC and 63 with CD. Patients were assessed for clinical remission and other outcomes at the 26-week mark post vedolizumab treatment initiation. At 26 weeks, clinical remission was achieved in 82.9% of UC patients and 85.7% of CD patients. Mucosal healing was observed in 38.1% of CD patients. The efficacy of vedolizumab did not significantly differ based on prior anti-tumor necrosis factor (anti-TNF) exposure. Notably, the clinical scoring tools for predicting vedolizumab response showed limited applicability in this cohort. Vedolizumab demonstrated high efficacy in treating both UC and CD in real-world settings in Bosnia and Herzegovina, underscoring its potential as a significant therapeutic option in IBD management.

Trains move in a specific way, along a pre-determined path, i.e. rails. The wheels of railway locomotives and rails are steel, and because of this, it is possible to achieve very high speeds, with relatively low resistance to movement. Analysis and research related to the movement of trains are very important from many aspects, especially for traffic safety. In this paper, a simulation of train movement under realistic conditions was performed. The simulation was created using the Python programming language. Infrastructure data, locomotive data, and resistances were used as input data, which Python later converts for simulation. The results of the simulation are presented both graphically and numerically. All data used in the Python simulation model was also input into the verified railway simulation software OpenTrack. The results from both tests were compared and analyzed, and a report was generated on the feasibility of using the created program in real-world scenarios.

Capacity of railway station is highly dependable on used interlocking system and traffic management pattern. Interlocking system's improvement requires significant financial resources, while traffic pattern could be changed by applying different management rules, depending on transport demand. This means that it is desirable to determine a certain trade-off between these parameters, up to the maximum capacity utilization index with the existing interlocking system. Several methods have been developed that consider given parameters for station capacity determination. In this paper we compare some of the methods for capacity determination, using the example of the Kalenic station, which was recently taken over by TENT from another industrial system.