Methods: This study involved 104 asymptomatic adults (females 62 [59.6%]) aged 18–50 years. Whole-spine standing lateral radiographs were obtained, and the pelvic, lumbar, thoracic, cervicothoracic, and cervical parameters were studied. Pearson’s correlation coefficient was used to assess correlations, with a significance threshold of p<0.05. Results: The mean age of participants was 38.27±9.93 years. The pelvic incidence (PI) significantly correlated with C7 slope (r=−0.212, p=0.05). The pelvic tilt (PT) exhibited significant correlations with T1 slope−CL mismatch (r=−0.229, p=0.05) and C2 slope (r=−0.202, p=0.05). Furthermore, PI–LL mismatch showed a significant correlation with TIA (r=−0.197, p=0.05), T1 slope (r=−0.228, p=0.05), and C7 slope (r=−0.251, p=0.05). Conclusions This study reveals a significant correlation between cervical and spinopelvic parameters, emphasizing the interconnectedness of pelvic, lumbar, thoracic, and cervical spine parameters.

Background and Objectives: Pericoronary adipose tissue (PCAT) is linked to coronary artery disease (CAD) progression through compositional changes that disrupt adipose homeostasis and affect vascular biology. This study explores how PCAT characteristics - volume and fat attenuation index (FAI)-relate to CAD severity and left ventricular (LV) function. Methods: This study included 100 patients with suspected CAD who underwent coronary computed tomography angiography (CCTA) and echocardiography between September 2023 and July 2024. PCAT volume, FAI and thickness were measured along coronary arteries (right coronary artery [RCA], left anterior descending [LAD], and left circumflex [LCX]). CAD severity was assessed using the Gensini score, while LV structural and functional metrics, including diastolic function, were obtained via echocardiography. Correlations between PCAT parameters, CAD burden, and LV function were analyzed using Spearman’s correlation and p value <0.05 was considered significant. Results: RCA FAI exhibited the strongest association with CAD severity (Gensini score: r=0.416, p=0.026) and diastolic dysfunction (r=0.536, p<0.001), with progressively less negative values from Grade 1 to Grade 3 dysfunction. LAD and LCX (r=0.278, p=0.03) FAI displayed moderate associations. RCA volume correlated with LV mass index, fractional shortening and diastolic parameters. Hypertension was associated with elevated RCA FAI and volume (p<0.05), while diabetes showed no significant impact on PCAT metrics. Conclusions PCAT FAI is a sensitive marker of CAD severity and diastolic dysfunction, especially in the RCA, highlighting its value for detection, risk stratification, and personalized CAD management. Integration of PCAT metrics into routine CCTA analysis could enhance cardiovascular risk assessment and treatment planning.

Methods: In total, 108 patients who underwent surgery for traumatic LLFs between January 2010 and January 2020 were reviewed to obtain their demographic details, injury level, and neurology status at the time of presentation (American Spinal Injury Association [ASIA] grade). Preoperative computed tomography scans were used to measure parameters such as anterior vertebral body height, posterior vertebral body height, loss of vertebral body height, local kyphosis, retropulsion of fracture fragment, interpedicular distance, canal compromise, sagittal transverse ratio, and presence of vertical lamina fracture. MRI was used to measure the canal encroachment ratio (CER), cross-sectional area of the thecal sac (CSAT), and presence of an epidural hematoma. Results: Of the 108 patients, 9 (8.3%) had ASIA A, 4 (3.7%) had ASIA B, 17 (15.7%) had ASIA C, 21 (19.4%) had ASIA D, and 57 (52.9%) had ASIA E neurology upon admission. The Thoracolumbar Injury Classification and Severity score (p =0.000), CER (p =0.050), and CSAT (p =0.019) were found to be independently associated with neurological deficits on the multivariate analysis. The receiver operating characteristic curves showed that only CER (area under the curve [AUC], 0.926; 95% confidence interval [CI], 0.860–0.968) and CSAT (AUC, 0.963; 95% CI, 0.908–0.990) had good discriminatory ability, with the optimal cutoff of 50% and 65.3 mm2, respectively. Conclusions Based on the results, the optimal cutoff values of CER >50% and CSAT >65.3 mm2 can predict the incidence of neurological deficits in LLFs.