AIM To investigate clinical and morphometric characteristics of patients with lower urinary tract symptoms (LUTS) due to lumbar spinal stenosis (LSS). METHODS This study evaluated LSS patients using clinical assessments of motor, sensory, bladder, and bowel functions, and functional disability scores from the Oswestry Disability Index (ODI) and Swiss Spinal Stenosis Questionnaire (SSSQ). Morphometric analysis included MRI measurements of the anteroposterior diameter of the intervertebral disc and dural sac, and the modified Torg-Pavlov ratio (mTPR), with follow-up re-evaluations at 6 months. RESULTS Of 159 patients, 49 (30.8%) had LUTS and 110 (69.2%) were in the control group. LUTS patients had a significantly higher prevalence of neurogenic claudication (100% vs. 47.3%; p<0.001), lower back pain (93.9% vs. 77.3%; p=0.011), and lower extremity pain (57.1% vs. 34.5%; p=0.008). The LUTS group also had higher ODI (54.0 vs. 50.0; p=0.019) and SSSQ score (44.0 vs. 34.0; p<0.001). Morphometric analysis showed significantly lower mTPR in LUTS patients (median 0.31 vs. 0.45; p<0.001), with an AUC of 0.704 (95%CI 0.627-0.774). mTPR ≤0.31 predicted surgical revision within 6 months (OR:3.4, CI: 1.2-9.8), motor deficiency (OR:2.1, 95%CI: 1.4-5.2), and persistent LUTS post-surgery (OR:4.5, 95%CI: 1.1-18.9). mTPR ≤0.34 was associated with worse follow-up outcome, including increased ODI (β:3.2; 95%CI: 1.1-5.3; p=0.004) and SSSQ score (β:4.8; 95%CI:2.1-7.5). CONCLUSION LUTS patients with LSS exhibit more severe symptoms and poorer outcome, with mTPR ≤0.34 being a predictor of adverse clinical outcome and the need for surgical revision within 6 months.

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ABSTRACT Background: The triglyceride/high-density lipoprotein (TG/HDL) ratio emerges as a promising marker for cardiovascular risk. However, the relationship between overall serum lipid levels and hemorrhagic stroke (HS) remains uncertain. Therefore, our study aims to explore the association between this novel index and mortality in HS patients. Methods: Utilizing a retrospective-prospective framework from January 2020 to August 2023, we scrutinized data from 104 hospitalized patients diagnosed with HS, with particular attention to their medical backgrounds and lipid profiles. Results: Age (odds ratio [OR], 1.078; 95% confidence interval [CI], 1.032–1.125; P = 0.001), atrial fibrillation (OR, 0.237; 95% CI, 0.074–0.760; P = 0.015), glucose level (OR, 1.121; 95% CI, 1.007–1.247; P = 0.037), and TG/HDL index (OR, 0.368; 95% CI, 0.173–0.863; P = 0.020) emerged as independent predictors for in-hospital mortality, as determined by both univariable and multivariable logistic regression analyses. Conclusion: Our results add weight to the growing evidence backing the utility of the TG/HDL index in assessing cardiovascular risk among HS patients. They emphasize the necessity of adopting a comprehensive risk assessment and management strategy that incorporates both traditional markers and novel indicators.

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Background and Objectives: This study aimed to investigate the novel adiponectin–resistin (AR) index as a predictor of the development of metabolic syndrome (MetS) in individuals with type 2 diabetes mellitus (T2DM). MetS is common in T2DM and increases cardiovascular risk. Adiponectin and resistin, adipokines with opposing effects on insulin sensitivity and inflammation, make the AR index a potential marker for metabolic risk. Materials and Methods: This prospective observational study included 80 T2DM participants (ages 30–60) from Sarajevo, Bosnia and Herzegovina, over 24 months. The participants were divided into two groups: T2DM with MetS (n = 48) and T2DM without MetS (n = 32). Anthropometric data, biochemical analyses, and serum levels of adiponectin and resistin were measured at baseline and every six months. The AR index was calculated using the formula AR = 1 + log10(R) − 1 + log10(A), where R and A represent resistin and adiponectin concentrations. Logistic regression identified predictors of MetS. Results: T2DM patients who developed MetS showed a significant decline in adiponectin levels (40.19 to 32.49 ng/mL, p = 0.02) and a rise in resistin levels (284.50 to 315.21 pg/mL, p = 0.001). The AR index increased from 2.85 to 2.98 (p = 0.001). The AR index and resistin were independent predictors of MetS after 18 months, with the AR index showing a stronger predictive value (p = 0.007; EXP(B) = 1.265). Conclusions: The AR index is a practical marker for predicting MetS development in T2DM participants, improving metabolic risk stratification. Incorporating it into clinical assessments may enhance early detection and treatment strategies.

Objective: Ventricular arrhythmias are the primary arrhythmias that cause sudden cardiac death. We address the problem of classification between ventricular tachycardia (VT), ventricular fibrillation (VF) and non-ventricular rhythms (NVR). Methods: To address the challenging problem of the discrimination between VT and VF, we develop similarity maps – a novel set of features designed to capture regularity within an ECG trace. These similarity maps are combined with features extracted through learnable Parzen band-pass filters and derivative features to discriminate between VT, VF, and NVR. To combine the benefits of these different features, we propose a hierarchical multi-stream ResNet34 architecture. Results: Our empirical results demonstrate that the similarity maps significantly improve the accuracy of distinguishing between VT and VF. Overall, the proposed approach achieves an average class sensitivity of 89.68%, and individual class sensitivities of 81.46% for VT, 89.29% for VF, and 98.28% for NVR. Conclusion: The proposed method achieves a high accuracy of ventricular arrhythmia detection and classification. Significance: Correct detection and classification of ventricular fibrillation and ventricular tachycardia are essential for effective intervention and for the development of new therapies and translational medicine.

Besides the quality of colour reproduction itself, there are other secondary print quality attributes. Secondary print quality evaluation is very important and is influenced primarily by the print method and type of substrate. For textile printers, there is an additional challenge related to macro non-uniformities due to the nature of the substrate. One of these secondary quality attributes is print mottle, which is influenced by macro non-uniformities that remain at the top layer of the print after the ink is fixed on the substrate. Print mottle values primarily consist of an analysis of macro non-uniformities and can be analysed using the Gray Level Co-occurrence Matrix (GLCM) method, among others. In this study, the GLCM method was used as well as the macro non-uniformity index or NU value verification method performed by ImageJ software. Four different textile printing methods and one cotton fabric substrate are used. The objective is to examine print mottle and the impact of printing method on macro non-uniformities. The printing methods include DTF, DTG, screen printing, and screen transfer printing. The aim is to compare the results of different printing methods and to determine their relation to perceived non-uniformity as assessed visually.