Background: Ischemic stroke remains the most feared complication of atrial fibrillation (AF), and thromboembolic risk is commonly estimated using clinical scores that may not fully capture the cardiometabolic dimension of cerebrovascular vulnerability. The aim of this research was to assess whether additional parameters can be used, to predict ischemic stroke risk in patients with AF, in order to explore whether TG/HDL-C may complement conventional clinical risk scores for ischemic stroke risk stratification in PAF, and to better characterize a metabolically high-risk phenotype beyond the recommendations provided by the CHA₂DS₂-VA score, which is useful but still far from perfect in predicting AF-associated ischemic stroke risk. Methods: In this retrospective, single-center observational study, we evaluated whether the triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDLc), a simple surrogate of atherogenic dyslipidemia and insulin resistance, is associated with ischemic stroke risk in patients with paroxysmal atrial fibrillation (PAF). We screened 1111 consecutive AF admissions between 1 January 2015 and 31 December 2016 and, from these 1111 AF cases, we extracted only the patients with PAF for analysis. Patients were stratified based on TG/HDLc values into two groups, Group 1 (TG/HDLc > 2.5; n = 155) and Group 2 (TG/HDLc < 2.5; n = 194). Statistical analysis was performed with MedCalc v23.4.0 using Chi-square and unpaired/Welch’s t-tests as appropriate, Pearson correlations, Kaplan–Meier analysis with log-rank testing, Cox regression for first ischemic stroke, and multivariable logistic regression to identify independent correlates of TG/HDLc > 2.5. Results: Patients with TG/HDLc > 2.5 had a significantly higher prevalence of ischemic stroke after AF onset compared with those with TG/HDLc < 2.5 (37.4% vs. 21.1%, p = 0.0008), despite similar CHA₂DS₂-VA and HAS-BLED scores, and also exhibited a higher burden of cerebrovascular and neurodegenerative findings, including cortical atrophy and cerebral lacunarism. Ischemic stroke-free survival curves diverged significantly over time (log-rank p = 0.0186), and an elevated TG/HDLc ratio was associated with a 68% higher hazard of first ischemic stroke (HR 1.68; 95% CI 1.09–2.60). In multivariable analysis, type 2 diabetes mellitus (OR 4.53), hyperuricemia (OR 3.83), dyslipidemia (OR 1.94), stroke (OR 1.77), and cortical atrophy (OR 4.48) were independently associated with TG/HDLc > 2.5. Conclusions: These findings suggest that TG/HDLc identifies a metabolically high-risk PAF phenotype associated with greater cerebrovascular burden and reduced ischemic stroke-free survival, providing an inexpensive and broadly available marker that may complement conventional clinical risk scores.

Background and Objectives: With the goal of identifying regions with bicortical bone and avoiding root contact, the present study proposes an innovative technique for the simulation of the insertion of mini orthodontic implants using automatic jaw segmentation. The simulation of mini implants takes place in 3D rendering visualization instead of Multi-Planar Reconstruction (MPR) sections. Materials and Methods: The procedure involves utilizing software that automatically segments the jaw, teeth, and implants, ensuring their visibility in 3D rendering images. These segmented files are utilized as study models to determine the optimum location for simulating orthodontic implants, in particular locations characterized by limited distances between the implant and the roots, as well as locations where the bicortical structures are present. Results: By using this method, we were able to simulate the insertion of mini implants in the maxilla by applying two cumulative requirements: the implant tip needs to be positioned in a bicortical area, and it needs to be situated more than 0.6 mm away from the neighboring teeth’s roots along all of their axes. Additionally, it is possible to replicate the positioning of the mini implant in order to distalize the molars in the mandible while avoiding the mandibular canal and the path of molar migration. Conclusions: The utilization of automated segmentation and visualization techniques in 3D rendering enhances safety measures during the simulation and insertion of orthodontic mini implants, increasing the insertion precision and providing an advantage in the identification of bicortical structures, increasing their stability.

Background/Objectives: Precise evaluation of mandibular morphology is essential for orthodontic diagnosis, growth assessment, and treatment planning. While lateral cephalograms are traditionally used for angular measurements, orthopantomograms (OPGs) allow side-specific assessment of mandibular structures. This study aimed to analyze bilateral gonial angles, bigonial width, and ramus heights on OPGs using artificial intelligence (AI)–assisted tracing, with a focus on symmetry, sex-related differences, and clinical applicability. Methods: A cross-sectional study was conducted on 78 Romanian patients (46 females, 32 males; mean age 22.8 ± 8.7 years) seeking orthodontic treatment. Standardized OPGs were obtained and analyzed using WebCeph AI-driven software. Bilateral gonial angles, bigonial width, and ramus heights were measured. Symmetry between sides and differences between sexes were evaluated, and correlation analyses were performed. Inter-rater reliability was assessed using intraclass correlation coefficients (ICCs). Results: The mean right and left gonial angles were 126.3° and 127.1°, respectively, with no significant sex- or side-related differences (p > 0.05). Bigonial width averaged 134.9 mm, being slightly larger in males, although not statistically significant (p = 0.240). Ramus heights were comparable bilaterally (right: 48.4 mm; left: 48.3 mm), with males showing slightly greater values. Correlation analysis demonstrated strong bilateral symmetry for gonial angles (r = 0.795) and ramus heights (r = 0.895). Negative correlations were observed between gonial angles and both bigonial width and ramus height, whereas bigonial width correlated strongly with ramus height. Measurement reproducibility was high (ICC > 0.75). Conclusions: OPGs, when combined with AI-assisted analysis, provide reliable and reproducible measurements of gonial angles and ramus heights, comparable to cephalometric values. Their ability to assess right and left sides separately enhances clinical utility in detecting asymmetries and monitoring mandibular growth. However, caution is advised when interpreting transverse parameters such as bigonial width, where CBCT remains the gold standard. AI-driven OPG analysis represents a cost-effective, accessible, and low-radiation diagnostic tool with significant potential for precision orthodontics.

Background: Chest pain is one of the most common causes of emergency room visits and also accounts for numerous visits to the family physician’s office or Outpatient Clinics of cardiology or internal medicine. Case Report: Here we present a case of a 48-year-old female patient who presented to our hospital emergency unit but refused hospital admission. She presented in our Outpatient Clinic with a complaint of typical chest pain indicating it to be of coronary origin. A computed tomography (CT) coronary angiography for the evaluation of this chest pain was indicated. While ruling out the coronary origin of this chest pain, we were surprised to have incidentally identified the presence of an esophageal tumor mass that had intimate contact with carina of the trachea. After the diagnosis of esophageal leiomyoma was made and its surgical treatment was performed, the patient was asymptomatic. Approximately one year after the surgical intervention was performed, following the cessation of antiplatelet therapy and statin, the patient returned to our Outpatient Clinic complaining of chest pain again with the same characteristics as previously presented, being terrified by the possibility of the recurrence of the esophageal leiomyoma. Upon resuming investigations, it was proven through coronary angio-CT evaluation that the etiology of the chest pain was indeed coronary this time. However, the patient still refused hospital admission and the performance of percutaneous coronary angiography with the potential implantation of a coronary stent. Conclusions: Chest pain can be due to various underlying pathologies and should not be neglected. A thorough investigation and timely management are key to treating this possible fatal symptom. In our case, the patient presented twice with the complaint of typical chest pain indicating a possible coronary event, but at the first presentation, it was due to esophageal leiomyoma, while a year later, the patient had similar pain, which was indeed this time due to coronary blockage. Hence, it is of utmost importance to think of all possible scenarios and to investigate accordingly, leaving no stone unturned.

Background and objectives: Artificial intelligence (AI) is increasingly embedded in public health workflows, yet adoption among practitioners remains uneven and is shaped by knowledge, legal awareness, and operational barriers. This cross-sectional study characterised determinants of AI adoption among healthcare professionals and examined how legal concern moderates the translation of technical knowledge into practical use, at a single Romanian tertiary academic centre. Methods: We surveyed 93 healthcare professionals (physicians, nurses, public health specialists, residents) at the “Pius Brînzeu” Clinical Emergency County Hospital and “Victor Babeș” University of Medicine and Pharmacy Timișoara. Participants were classified as AI adopters or non-adopters. Likert-derived composite scores (0–100; Cronbach’s α 0.79–0.88) quantified knowledge, trust, legal concern, privacy concern, and workflow confidence. Group comparisons used independent-samples t-tests and χ² tests; associations used Pearson correlation; predictors of adoption and usage intensity were modelled with logistic and multiple linear regression; a two-way ANOVA tested profession-by-training effects. Significance was set at p < 0.05. Benjamini–Hochberg false-discovery-rate correction was applied across the 18 bivariate tests reported in this study, and adjusted q-values are reported alongside unadjusted p-values. Results: Adopters (n = 51) were younger (34.7 ± 7.5 vs. 43.2 ± 8.5 years; p < 0.001) and reported higher knowledge (67.3 vs. 48.6; p < 0.001) and workflow confidence (64.2 vs. 41.9; p < 0.001) but lower legal concern (58.4 vs. 71.2; p < 0.001). Knowledge correlated positively with usage intensity (r = 0.536; p < 0.001), whereas legal concern correlated negatively (r = −0.426; p < 0.001). In multivariable models, younger age (OR = 0.91; p = 0.004), knowledge (OR = 1.06; p = 0.005), and trust (OR = 1.07; p = 0.005) independently predicted adoption. The linear model explained 46.1% of usage variance. Stratified analysis suggested legal concern attenuated the knowledge–usage slope (β: 0.51→0.18); however, the formal knowledge-by-concern interaction term was not statistically significant (p = 0.191), and this pattern is therefore exploratory. Conclusions: In this modest, single-centre sample, AI adoption was independently associated with knowledge and trust, and legal concern was independently and negatively associated with usage intensity; the apparent dampening of the knowledge–usage relationship by legal concern was suggestive but not statistically confirmed. Targeted legal-regulatory literacy and structured training may support practical AI uptake in public health settings, pending confirmation in larger, multicentre studies.

Background/Objectives: Early identification of breast cancer patients who are likely or unlikely to benefit from neoadjuvant chemotherapy (NAC) remains clinically important because ineffective treatment may delay definitive surgery and expose patients to unnecessary toxicity. Quantitative ultrasound (QUS) radiomics offers a contrast-free and repeatable method for extracting tissue-sensitive imaging biomarkers from raw ultrasound data. This study aimed to evaluate whether baseline QUS radiomic features and early treatment-induced changes could predict a pathologic response to NAC in a real-world single-center cohort. Methods: We designed a prospective observational study including 96 consecutive women with biopsy-proven stage II–III breast cancer treated with NAC at Victor Babes University of Medicine and Pharmacy Timisoara. All patients underwent standardized QUS examinations before treatment and again at week 2. The response was defined pathologically at surgery as residual cancer burden class 0/I versus II/III. Clinical, histopathologic, and QUS variables were compared between responders and non-responders. Group comparisons used Student’s t test, Mann–Whitney U test, chi-square testing, and Fisher’s exact test where appropriate. Multivariable logistic regression was used to identify independent predictors of response. Model discrimination was summarized using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: Forty-three patients (44.8%) were classified as responders and 53 (55.2%) as non-responders. Responders had higher baseline Ki-67 values (47.8 ± 13.1% vs. 41.9 ± 13.0%, p = 0.033), lower baseline homogeneity (0.3 ± 0.1 vs. 0.4 ± 0.1, p = 0.010), and higher peritumoral heterogeneity (0.9 ± 0.1 vs. 0.8 ± 0.2, p = 0.027). At week 2, responders showed larger increases in mid-band fit (3.0 ± 0.8 vs. 1.2 ± 0.8 dB, p < 0.001), greater entropy change (0.7 ± 0.2 vs. 0.2 ± 0.2, p < 0.001), more pronounced spectral intercept reduction (−3.5 ± 1.4 vs. −1.2 ± 1.3, p < 0.001), and greater tumor shrinkage (−24.3 ± 7.0% vs. −11.1 ± 5.7%, p < 0.001). In multivariable analysis, Δ MBF and Δ entropy remained independent predictors of pathologic response. The combined clinical-plus-QUS model achieved an AUC of 0.89. Conclusions: Baseline microstructural heterogeneity and very early QUS-derived treatment changes were strongly associated with the pathologic response to NAC. These findings support the potential role of QUS radiomics as a low-cost, repeatable early-response biomarker in breast cancer.