An Analysis of Optic Disc Parameters in Patients with Peripheral Retinal Tears Following Acute Posterior Vitreous Detachment: A Cross-Sectional Study.

Background: To investigate association between optic disc parameters analyzed by optical coherence tomography (OCT) and occurrence of peripheral retinal tears in patients with symptomatic posterior vitreous detachment (PVD). Methods: This cross-sectional study enrolled 75 patients with symptoms of acute PVD, who were allocated into two groups based on whether a peripheral retinal tear occurred or not. Results: When comparing the average retinal nerve fiber layer (RNFL) thickness (µm) between retinal tear and control groups, it was shown that patients with a retinal tear have a significantly higher (87.18 [95% confidence interval (CI), 84.47 to 89.9] vs 81.14 [95% CI, 77.81 to 84.46], P = 0.005) average RNFL thickness. Furthermore, we observed a significant difference (0.13, 0.06 to 0.22 vs 0.07, 0.04 to 0.1, P = 0.036, Mann-Whitney U-test) in the size of cup volume (mm3) between the tear and control groups, respectively. Linear regression showed a significant decrease (P = 0.029) in average RNFL thickness with increasing age, but without a significant difference between the two groups. There was no statistically significant difference between the tear and control groups in terms of rim area, disc area, and average cup-to-disc ratio. Conclusion: Patients with a higher average RNFL thickness and larger cup volume measured by OCT were more prone to develop a peripheral retinal tear. Increased peripapillary average RNFL thickness due to trauma and subsequent inflammation, possibly related to the more adherent posterior hyaloid membrane to the retina, may also indicate strengthened adhesions in the areas of the peripheral retina where retinal tears occur. OCT analysis of the optic nerve head may be used in everyday clinical practice as a predictor of the development of peripheral retinal tears in patients with symptomatic PVD.

The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review.

Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71-0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74-0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82-0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.

Adherence to Mediterranean diet and advanced glycation endproducts in patients with diabetes.

BACKGROUND: In recent years, American Diabetes Association started to strongly advocate the Mediterranean diet (MD) over other diets in patients with diabetes mellitus (DM) because of its beneficial effects on glycemic control and cardiovascular (CV) risk factors. Tissue levels of advanced glycation endproducts (AGEs) emerged as an indicator of CV risk in DM. Skin biopsy being invasive, the use of AGE Reader has been shown to reflect tissue AGEs reliably. AIM: To examine the association between adherence to MD and AGEs in patients with DM type II. METHODS: This cross-sectional study was conducted on 273 patients with DM type II. A survey questionnaire was composed of 3 separate sections. The first part of the questionnaire included general data and the habits of the participants. The second part aimed to assess the basic parameters of participants' diseases and associated conditions. The third part of the questionnaire was the Croatian version of the 14-item MD service score (MDSS). AGEs levels and associated CV risk were measured using AGE Reader (DiagnOptics Technologies BV, Groningen, The Netherlands). RESULTS: A total of 27 (9.9%) patients fulfilled criteria for adherence to MD, with a median score of 8.0 (6.0-10.0). Patients with none/limited CV risk had significantly higher percentage of MD adherence in comparison to patients with increased/definite CV risk (15.2% vs 6.9%, P = 0.028), as well as better adherence to guidelines for nuts (23.2% vs 12.6%, P = 0.023) and legumes (40.4% vs 25.9%, P = 0.013) consumption. Higher number of patients with glycated hemoglobin (HbA1c) < 7% adhered to MD when compared to patients with HbA1c > 7% (14.9% vs 7.3%, P = 0.045). Moreover, those patients followed the MDSS guidelines for eggs (33.0% vs 46.8%, P = 0.025) and wine (15.6% vs 29.8%, P = 0.006) consumption more frequently. MDSS score had significant positive correlation with disease duration (r = 0.179, P = 0.003) and negative correlation with body mass index (BMI) values (r = -0.159, P = 0.008). In the multiple linear regression model, BMI (ß ± SE, -0.09 ± 0.04, P = 0.037) and disease duration (ß ± SE, 0.07 ± 0.02, P < 0.001) remained significant independent correlates of the MDSS score. Patients with HbA1c > 7% think that educational programs on nutrition would be useful for patients in significantly more cases than patients with HbA1c < 7% (98.9% vs 92.6%, P = 0.009). CONCLUSION: Although adherence to MD was very low among people with diabetes, we demonstrated that adherence to MD is greater in patients with lower CV risk, longer disease duration, and well-controlled glycaemia.