Medicina
-
The incidence and societal burden of cancer is increasing globally. Surgery is indicated in the majority of solid tumours, and recent research in the emerging field of onco-anaesthesiology suggests that anaesthetic-analgesic interventions in the perioperative period could potentially influence long-term oncologic outcomes. While prospective, randomised controlled clinical trials are the only research method that can conclusively prove a causal relationship between anaesthetic technique and cancer recurrence, live animal (in vivo) experimental models may more realistically test the biological plausibility of these hypotheses and the mechanisms underpinning them, than limited in vitro modelling. This review outlines the advantages and limitations of available animal models of cancer and how they might be used in perioperative cancer metastasis modelling, including spontaneous or induced tumours, allograft, xenograft, and transgenic tumour models.
-
Background and objectives: In the last couple of years, pharmacological management of patients with type 2 diabetes mellitus (T2DM) have been markedly renewed. The aim of this study was to analyse the changes in prescribing patterns of antidiabetic drugs for treating patients with T2DM in Hungary between 2015 and 2020. Material and Methods: In this retrospective, nationwide analysis, we used the central database of the National Health Insurance Fund. ⋯ After initial MET monotherapy, the incidence rates of patients with add-on GLP-1-RAs (2%, 3%, and 4%) and those of add-on SGLT-2 inhibitors (4%, 6%, and 8%) slowly increased in the subsequent 24, 48, and 72 months, respectively. Conclusions: In the period of 2015−2020, we documented important changes in trends of antihyperglycaemic therapeutic patterns in patients with T2DM which followed the new scientific recommendations but remained below our expectations regarding timing and magnitude. More efforts are warranted to implement new agents with cardiovascular/renal benefits into therapeutic management in time, in a much larger proportion of T2DM population, and without delay.
-
Background and Objectives: The aim of this study was to evaluate the impact of sagittal imbalance based on pelvic incidence−lumbar lordosis (PI-LL) mismatch on the analgesic efficacy of epidural steroid injection in geriatric patients. Materials and Methods: Patients aged 65 years or older who received lumbar epidural steroid injections under fluoroscopy were enrolled. The cutoff of PI-LL mismatch >20° was used as an indicator of a marked sagittal imbalance. ⋯ There was no difference in analgesic outcome after injection according to the PI-LL mismatch (good analgesia 60.0 vs. 60.9%, p = 0.889). Multivariate analysis showed that pre-injection opioid use, moderate to severe foraminal stenosis, and high-graded paraspinal fat infiltration were significantly associated with poor analgesia after injection. Conclusions: There was no significant correlation between sagittal spinopelvic alignment and pain relief after lumbar epidural steroid injection for geriatric patients.
-
Case Reports
Ascites as First Atypical and Only Clinical Manifestation of De Novo Follicular Lymphoma.
Follicular lymphoma is the most common indolent non-Hodgkin's lymphoma and is usually initially detected in lymph nodes. Primary extranodal NHL is most commonly primarily localized in the gastrointestinal tract. We present one unusual case of ileum FL with ascites as the first clinical sign. ⋯ The microscopy finding of terminal ileum and the regional lymph nodes showed domination of cleaved cells with irregular nuclei which correspond to centrocytes. There were 0-15 large non-cleaved cells corresponding to centroblast in the microscopy high-power field. The final diagnosis was follicular lymphoma, the clinical stage 2E and histological grade by Berard and Mann criteria 1-2.
-
Background and Objectives: We developed a machine learning algorithm to analyze trauma-related data and predict the mortality and chronic care needs of patients with trauma. Materials and Methods: We recruited admitted patients with trauma during 2015 and 2016 and collected their clinical data. Then, we subjected this database to different machine learning techniques and chose the one with the highest accuracy by using cross-validation. ⋯ The complete model exhibited an 86% recall for recovery, 30% for chronic care, 67% for mortality, and 80% for complications; the short-term model fitted for ED displayed an 89% recall for recovery, 25% for chronic care, and 41% for mortality. Conclusions: We developed a machine learning algorithm that displayed good recall for the healthy recovery group but unsatisfactory results for those requiring chronic care or having a risk of mortality. The prediction power of this algorithm may be improved by implementing features such as age group classification, severity selection, and score calibration of trauma-related variables.