Karabük University

C-reactive protein and uric acid to albumin ratio (CUAR) is a recent inflammatory marker associated with cardiovascular disease. We aim to investigate the relationship between CUAR and coronary collateral circulation (CCC) in patients with stable coronary artery disease and chronic total occlusion (CTO).

The patients were divided into two groups; 267 patients with poor CCC formation group and 133 patients with well CCC formation group. CUAR log was calculated using the ‘log10 (CRP × UA/Albumin)’ formula.

CUAR log levels were significantly higher in patients with poor CCC formation ( P  < 0.001). CUAR log levels above 1.45 predicted poor CCC with a higher sensitivity of 70% and specificity of 67% than uric acid to albumin ratio (UAR), neutrophil to lymphocyte ratio (NLR), and platelet to lymphocyte ratio (PLR) ( P  < 0.001). In the comparison of the AUC values, there was a significant difference between the CUAR log and UAR ( P  = 0.047), NLR ( P  = 0.001), and PLR ( P  < 0.001). In multivariate regression analysis, CUAR log above 1.45 ( P  < 0.001) was associated independently with poor CCC occurrence.

Our results suggested that CUAR log is a more potent and independent marker than other inflammatory markers to predict poor CCC occurrence in CTO patients. It may be useful to identify high-risk patients with poor CCC occurrence.

Sex estimation plays a critical role in forensic identification, missing person identification, and forensic investigations. This study aimed to evaluate the usability of quantitative metric measurements obtained from computed tomography (CT) images of the atlas (the first cervical vertebra) for sex identification using machine learning algorithms. The study used CT images from 200 individuals (100 males and 100 females). Eighteen metric parameters of the atlas-comprising 16 lengths and 2 angles-were measured. These parameters were analyzed using 13 different machine learning algorithms. Basic statistical methods were used to compare the effect of each metric parameter on sex estimation. The machine learning models predicted sex with an accuracy ranging from 86% to 89%. The highest accuracy (89%) was achieved by the Gaussian Naive Bayes algorithm using only five selected metric parameters. Additionally, 15 out of the 18 measured parameters showed statistically significant differences between sexes. This study demonstrates that sex can be estimated with high accuracy using only quantitative metric measurements data from the atlas vertebra and machine learning algorithms. Notably, this approach may be especially valuable when only the atlas is available, providing essential preliminary data for forensic medical examinations.

In emergency medicine, the belief that some physicians are "Black Clouds" consistently facing heavier workloads, while others are "White Clouds" experiencing lighter shifts, persists despite limited scientific validation.

This study aimed to evaluate whether these reputational labels correspond to measurable differences in clinical workload.

A retrospective observational study was conducted in a tertiary emergency department with an average of 830 daily visits. Physicians with at least 6 months of service were surveyed to identify peers as "luckiest" (White Clouds) and "unluckiest" (Black Clouds), resulting in four physicians per group. Duty rosters over 6 months were classified as black, white, or gray days depending on on-duty combinations. Objective parameters-including patient volume, red triage cases, admissions, cardiopulmonary resuscitations, trauma cases, and deaths-were extracted from electronic records and statistically compared.

A total of 156 shifts were analyzed: 57 white, 51 gray, and 48 black days. Mean daily patient volume was slightly lower on black days (814.31 ± 8.90) compared to white (825.91 ± 9.10) and gray (829.09 ± 10.24), though not statistically significant (p = 0.429). No significant differences were found in red triage patients, admissions, intensive care unit (ICU) admissions, resuscitations, trauma cases, or deaths across groups.

The study found no statistically significant differences between Black Cloud and White Cloud shifts. These labels appear to reflect subjective perception rather than objective workload, underscoring the need to address cultural superstitions in emergency medicine to reduce stigma and support physician well-being.