Ki67 is a well-established proliferation marker in breast cancer. Current clinical use focuses on the proportion of Ki67-positive cells, ignoring spatial heterogeneity in expression. However intra-tumoral heterogeneity has demonstrated to be associated with worse outcome. We hypothesized that spatial Ki67 heterogeneity carries clinical information beyond conventional scoring and aimed to evaluate its added value for predicting pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) and for stratifying recurrence risk using genomic expression profiling (GEP). Using digital image analysis (DIA), precise and spatial quantification of biomarker distribution is possible. We analyzed two retrospective breast cancer cohorts using an AI-assisted DIA pipeline. Tumor sections stained for ER, PR, Ki67, and HER2 were digitized and analyzed in QuPath. Using AI, individual tumor cells were recognized and four tumor regions (0.5mm x 0.5mm) with the highest tumor/stroma ratio were selected for analysis. Spatial Ki67 heterogeneity was quantified using the Morisita-Horn Index (MHI) after the Ki67-positive and Ki67-negative tumor cells were mapped using XY-coordinates and square tessellation (100×100 µm tiles) was applied. The MHI was used to compare the similarity in cell composition between all pairs of tiles within a region. MHI values range from 0 to 1, with higher values indicating a more uneven distribution of Ki67+ cells. We used logistic regression and model comparison with Akaike Information Criterion (AIC), likelihood ratio test (LRT) or Vuong test, to evaluate the predictive value of Ki67 heterogeneity. In the first cohort (n=45), spatial heterogeneity was assessed on pretreatment biopsies from patients treated with NACT. In the second cohort (n=79), heterogeneity was evaluated in HR+/HER2- breast cancer patients stratified as high or low risk of recurrence based on GEP. In the GEP cohort, both a higher proportion of Ki67- positive cells and greater Ki67 heterogeneity were significantly associated with high genomic risk. The median MHI was 0.24 (0.03–0.35) in the high-risk group compared to 0.14 (0.01–0.43) in the low-risk group (P = 0.008). This higher MHI indicates more heterogeneous regionally clustered Ki67 expression, suggesting biologically distinct proliferative zones. In multivariate models, Ki67 heterogeneity remained a significant predictor of high-risk classification (OR 0.22, P = 0.036). Furthermore, in nested model comparison using LRT, addition of Ki67 heterogeneity significantly improved the model for predicting genomic risk (P = 0.034). These findings were consistent across biopsy and resection specimens, highlighting the robustness of heterogeneity measures. In the NACT cohort, Ki67 heterogeneity was higher in patients who achieved pCR (median MHI 0.26 [0.17–0.35]) compared to those who did not (median MHI 0.22 [0.12–0.40], P = 0.023). In multivariate modeling, Ki67 heterogeneity emerged as an independent predictor of pCR (OR 23.5, P = 0.038), outperforming Ki67 density and improving model fit (AIC 31.6 vs. 36.1; P = 0.038). Finally, in both cohorts, DIA-derived Ki67 models slightly outperformed traditional pathologist scoring, although Vuong tests did not show a statistically significant difference. Spatial Ki67 heterogeneity provides additional prognostic and predictive value beyond conventional Ki67 scoring. This heterogeneity indicates distinct areas of higher proliferation, clinically relevant biological variation, not captured by simple percentage positivity. Although validation in larger, prospective cohorts is necessary before clinical implementation, DIA provides a more objective and reproducible alternative to manual scoring, particularly when incorporating spatial heterogeneity. C. Van Berckelaer, K. Zwaenepoel, L. Cox, D. Charlotte, D. Julie, E. Louise, H. Fleur, L. Evy, G. R. Devi, A. Ramadhan, S. Koljenovic, P. Van Dam. Ki67 Spatial Heterogeneity as a Predictive and Prognostic Marker in Breast Cancer: A Spatial Image Analysis Approach [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS2-08-19.

<p><strong>Introduction. </strong>The development of information technologies in education has enabled the introduction of new teaching approaches, among which the Flipped classroom (FC) model has gained increasing attention. The FC model has emerged as a student-centered approach that promotes active learning in medical education. The&nbsp;<br />aim of this study was to examine students&rsquo; perceptions of the FC implementation in medical education.<br /><strong>Method.</strong> A cross-sectional study was conducted on a sample of 63 third-year medical students at the Faculty of Medicine in Foča. Data were collected through an anonymous online questionnaire distributed via the Moodle platform, consisting of six domains and a total of 31 statements.<br /><strong>Results. </strong>Students expressed generally positive attitudes toward the FC model: over 50% provided positive responses, about one-third were neutral. The highest average scores were related to learning independence&nbsp;<br />(x̄ = 3.84) and preparedness and motivation for classes (x̄ = 3.79). No statistically significant differences were&nbsp;<br />found between male and female students&rsquo; attitudes, while students with a higher-grade point average (&ge; 8.50) showed significantly more favorable attitudes in the domains of overall attitudes (p = 0.036) and communication (p = 0.013). Logistic regression analysis indicated that the communication domain was a significant predictor of belonging to the group with higher academic achievement (p = 0.020).<br /><strong>Conclusion.</strong> The results indicate that medical students perceive the FC positively and recognize its contribution&nbsp;<br />to better motivation, independence, and interactivity in the learning process. These findings may serve as a basis for further research on the impact of this model on academic outcomes and for a deeper understanding of students&rsquo; perceptions of modern learning approaches in medical education.</p>

<p><strong>Introduction. </strong>The increasing use of digital devices among university students has raised concerns regarding its potential impact on physical and mental health. However, the independent contribution of different patterns of screen use remains insufficiently understood. This study aimed to examine screen use patterns among medical students and to assess their associations with selected health outcomes, with a particular focus on identifying independent predictors.<br /><strong>Methods. </strong>A cross-sectional study was conducted among 96 medical students aged 19&ndash;26 years. Data were collected using a self-administered questionnaire assessing daily screen time, timing of use, physical activity, and health-related outcomes. Multivariate binary logistic regression models were used to identify independent predictors of sleep disturbances, anxiety, and musculoskeletal pain.<br /><strong>Results.</strong> The median daily screen time was five hours. The most frequently reported health issues were eye strain (56.3%), musculoskeletal pain (53.1%), sleep disturbances (46.9%), and anxiety (40.6%). A weak but statistically significant positive correlation was observed between screen time and sleep disturbances (rs = 0.209, p = 0.044, N = 93 due to missing data for three participants). In multivariate analysis, late-night screen use was identified as an independent predictor of sleep disturbances (OR = 9.37, 95% CI: 1.96&ndash;44.75, p = 0.005), whereas total screen time was not significant after adjustment. No independent predictors were identified for anxiety or musculoskeletal pain.<br /><strong>Conclusion. </strong>The findings suggest that the impact of screen use on health outcomes is domain-specific. Behavioral patterns, particularly late-night use, appear to be more relevant than total screen time in relation to sleep disturbances. These results highlight the importance of a behavior-oriented approach to digital media use among students.</p>

<p><strong>Introduction.</strong> The most common malignant facial skin tumors are basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma. Surgical excision remains the gold standard of treatment, followed by reconstruction of the resulting defect. The aims of this study were to present reconstructive options for facial defects following excision of malignant skin tumors and to evaluate functional and aesthetic outcomes of local flap reconstruction.<br /><strong>Methods</strong>. This retrospective case series included 80 patients surgically treated at Varis Clinic in Belgrade and at the Department of Plastic and Reconstructive Surgery, University Hospital in Foča, from January 2021 to October 2025. Patients were analyzed with respect to tumor type, defect size and localization, sex, age, and postoperative complications. Reconstruction was performed using local flaps.<br /><strong>Results.</strong> Facial defects resulted from excision of BCC in 51 patients, SCC in 24 patients, and melanoma in five patients. Complete flap survival was achieved in all cases (100%). Postoperative infection with marginal flap necrosis occurred in three patients (3.75%) and resolved after conservative treatment. Functional and aesthetic outcomes were satisfactory in all patients.<br /><strong>Conclusion.</strong> Local flaps represent a reliable reconstructive method for small to large facial defects. Proper surgical planning, anatomical knowledge, and meticulous technique are essential for achieving optimal functional and aesthetic outcomes.</p>

Artificial intelligence (AI) could facilitate and objectify quality assessment in the daily routine. The purpose was to explore the extent to which an AI prototype algorithm is able to replicate the perfect-good-moderate-inadequate (PGMI) system (perfect, good, moderate, inadequate). From a multicentre case collection, 200 standard mammograms (800 images) were selected. A deep learning-based prototype software was used to rate the images in analogy to the PGMI system. The AI results were compared with a reference standard obtained through consensus reading by three expert radiographers and one expert radiologist, using quadratically weighted Cohen’s kappa with confidence intervals (CI) and context-based interpretation. Frequency and reasons for disagreement were evaluated for challenging cases with a discrepancy of two or more grades and a discrepancy in assigning an inadequate. For overall PGMI per image, slight agreement between human consensus and AI was observed for CC views (κ = 0.14) and fair agreement for MLO views (κ = 0.25). The highest agreement was observed for the CC category “M. Pectoralis visibility” (substantial, κ = 0.75). Best category in MLO was “Pectoralis angle” (moderate, κ = 0.49). For other categories, fair, slight or poor agreement was observed. The work-up of disagreement gave insight into misinterpretations of anatomical landmarks and causality issues in the categorization. Transforming the PGMI system into a fully automated AI algorithm is challenging and may differ substantially between subcategories. Further research in computer science and quality assessment methodology is needed to pave the way for AI-based objective quality management in mammography. Profound evaluation of AI algorithms and their ability to replicate human interpretation, scoring, and classification are the basis and scientific framework toward AI-based objective quality management in mammography. AI has huge potential for automated assessment of diagnostic image quality. Compared with human reading agreement, substantial disagreement may also be found. Direct transformation of perfect-good-moderate-inadequate scoring into an AI algorithm is challenging. AI has huge potential for automated assessment of diagnostic image quality. Compared with human reading agreement, substantial disagreement may also be found. Direct transformation of perfect-good-moderate-inadequate scoring into an AI algorithm is challenging.

Although persons with intellectual disabilities are entitled to sexual education and freedom of sexual expression, they are often discriminated against in this area and denied access to appropriate education. The attitudes of professional staff play a crucial role in shaping how sexuality is addressed in educational, social and care settings. Supportive and informed professional attitudes are essential for promoting healthy sexual development and safeguarding the well-being of persons with intellectual disabilities. The aim of this study was to examine the attitudes of professional staff who provide support to persons with intellectual disabilities toward the sexuality in relation to the respondents’ gender and age. To assess professionals’ attitudes toward the sexuality of persons with intellectual disabilities adopted version of ASQ-ID (Attitudes to Sexuality Questionnaire – Individuals with an Intellectual Disability) developed by Cuskelly and Gilmore (2007) was used. The study included a sample of 90 respondents (various profiles of professional staff who providing support to persons with intellectual disabilities). The results showed that there are differences in the attitudes of professional staff in relation to the age of respondents, while no statistically significant differences were found in relation to gender of professional staff.

Graphical abstract

Transcriptomic studies have helped us understand the dorsal root ganglia’s cellular milieu, yet our knowledge of protein expression and spatial organization/architecture remains less defined. Here we establish a comprehensive resource from processing through analysis of hDRG tissue. We optimize tissue-handling strategies and evaluate 114 antibodies targeting neuronal and non-neuronal cell types, identifying protocols that preserve neuronal morphology and antigen retain specificity. Integrating these workflows with our Deep Learning-assisted image analysis pipelines, we quantify size, expression, and spatial organization across 35,721 neurons from 15 donors. Female donors exhibited significantly larger neuronal somata, indicating sexual dimorphism. Neuronal subpopulations display clear spatial clustering. We further characterized the perineuronal niche, marked by dense vascularization, nuclear remodeling in perineuronal cells, and age-related increased turnover of neuron-associated macrophages. Together, this resource provides standardized methodologies and quantitative frameworks for reproducible protein-level interrogation of human sensory biology and pain mechanisms.

Anti-inflammatory activity of acetone extract of plant root sorts Polentilla speciosa Villd. and Potentilla tommasiniana FW.Schultz, Rosaceae was examined. The examined material was picked up in autumn in the surroundings of Sarajevo, dried in thin layer and pulverized immediately before the experiment. Swiss albino mice were used as experimental animals. The examinations word performed on mouse car in groups as presented in the Table 1. As comparing substance 1% hydro-cortisone cream was used. The other ear of the same animal was used as control one. It is found that acetone docs not influence the process of inflammation. The achieved results are presented by changes in car appearance after three days from the moment of examined extracts application. The treated car looked significantly better than untreated car. The examined mice groups and used substances are presented in Table 1. This method of local anti-inflammatory activity examination on mouse car is very suitable for examination because it gives data even for small sample quantities. Examined acetone extracts of plant sorts Potentilla speciosa Villd. and Potentilla tommasiniana FW. Schultz, Rosaceae showed to possess anti-inflammatory activity, and the achieved results can be objectively shown by photographs of the examined samples. Comparing the achieved results, we can come to the conclusion that acetone extract of the plant root Potentilla speciosa Villd. Showed stronger anti-inflammatory activity than the extract of plant root Potentilla tommasiniana FW. Schultz, Rosaceae.

ABSTRACT The European Renal Association (ERA) Registry collects data on patients with kidney failure receiving kidney replacement therapy (KRT). This paper presents a summary of the ERA Registry Annual Report 2023, and focuses specifically on comparisons by age. The complete ERA Registry Annual Report 2023 is available in the Supplementary information. For 2023, data were collected from 34 countries in Europe and countries bordering the Mediterranean Sea. Using these data, incidence and prevalence of KRT, kidney transplantation rates, survival probabilities, and expected remaining lifetimes were calculated. In 2023, the ERA Registry covered 519 million people in the participating countries. The incidence of KRT was 151 per million population (pmp). Among incident patients, 29% were aged ≥75 years, 64% were male, and the most common primary renal disease (PRD) was diabetes mellitus (22%). Most patients (83%) started KRT with haemodialysis (HD), 11% started with peritoneal dialysis (PD), and 6% underwent pre-emptive kidney transplantation. On 31 December 2023, the prevalence of KRT was 1101 pmp. Among prevalent patients, 24% were aged ≥75 years, 62% were male, and the most common PRD was of miscellaneous origin (18%). Moreover, 56% of prevalent patients received HD, 5% received PD, and 39% were living with a functioning graft. In 2023, the kidney transplantation rate was 43 pmp, with 69% of kidneys coming from deceased donors. For patients starting KRT between 2014 and 2018, 5-year survival probability was 51%. The proportions of incident and prevalent patients aged ≥75 varied considerably across European countries. In addition, incident patients aged ≥75 were more often male, and had more often hypertension as PRD compared with younger patients. Only 1% of incident patients aged ≥75 received a pre-emptive kidney transplant, while among prevalent patients of the same age, 22% was living with a functioning graft.

Background Proton pump inhibitors (PPIs) are widely used for the treatment of acid-related disorders, but inappropriate or prolonged use carries potential health risks. Physicians, due to their access to medication and clinical knowledge, may be prone to self-medicating with PPIs without appropriate oversight. Objective To assess the prevalence and patterns of personal PPI use and self-medication among practicing physicians in Bosnia and Herzegovina, and to identify demographic and professional predictors of such behavior. Methods A cross-sectional, questionnaire-based survey was conducted among 448 physicians who responded to the study invitation, out of approximately 600 invited, from various healthcare levels in Bosnia and Herzegovina between January and May 2025. The survey collected data on PPI use history, consultation behavior, awareness of adverse effects, and adherence to treatment guidelines. Multivariable logistic regression was used to identify independent predictors of self-medication. Results A total of 65.4% of respondents reported past PPI use, during their medical practice, and 31.7% were current users. Over half (52.2%) admitted using PPIs without consulting another physician, and only 17.4% referred to clinical guidelines prior to use. Occasional use was the most common pattern (59.0%), while adverse effects were rarely reported (1.8%). No demographic or professional variable was significantly associated with self-medication with PPIs (defined as PPI use without consulting another physician) in the multivariable analysis. Conclusion Self-medication with PPIs is highly prevalent among physicians and frequently occurs without clinical consultation or adherence to guidelines. This behavior appears to be widespread across age groups, sexes, and care levels, highlighting the need for institutional interventions that promote rational prescribing and raise awareness about responsible self-care within the medical profession.

Background The long-term use of beta blockers after myocardial infarction in patients with preserved ventricular function is debated. General practitioners (GPs) often decide whether to continue or discontinue long-term medications, yet little is known about how they apply evolving evidence to clinical prescribing decisions. Objective To assess whether GPs are willing to deprescribe beta blockers post myocardial infarction with preserved left ventricular function and to identify factors associated with deprescribing decisions. Design Cross-sectional online survey using case vignettes, conducted between July 2023 and October 2024 in primary care settings in 24 sites across 20 European countries. Participants Practicing GPs recruited through convenience sampling at each site. Main measures The primary outcome was whether the GP chose to deprescribe beta blockers in the vignettes. Adjusted risk ratios for the association between GP characteristics and the decision to deprescribe were estimated using Poisson regression with generalized estimating equations and robust standard errors, accounting for clustering at the GP and country level. Key results 604 GPs participated in the survey (median [IQR] age, 44.0 [35.0-54.8] years; 364 [60.3%] female), 89.2% deprescribed beta blockers in at least one vignette. The likelihood of deprescribing increased with time since myocardial infarction (adjusted risk ratio [RR] = 1.28; 95% CI 1.21–1.36 after 5 years; RR = 1.78; 95% CI 1.66–1.90 after 10 years vs. 3 months) and with side effects (RR = 1.76; 95% CI 1.66–1.88). More years of clinical experience were associated with a lower likelihood of deprescribing (RR = 0.86; 95% CI 0.77–0.95 for most vs. least experienced). Conclusions In this cross-national vignette study, most GPs were willing to deprescribe beta blockers after myocardial infarction in patients with preserved left ventricular function, particularly when time had passed and side effects were present. These findings suggest that GPs are open to applying evolving evidence on beta blocker discontinuation in clinical care. Supplementary Information The online version contains supplementary material available at 10.1186/s12875-026-03208-6.

The automotive industry is undergoing a significant transformation towards electric vehicles (EVs) with the main goal of reducing greenhouse gas emissions and for a sustainable and green environment. Different types of EVs are introduced every day in the market where selecting an optimal vehicle for purchase constitutes a complex decision-making. Therefore, the purpose of this research was to evaluate EVs in Albania using multi-criteria decision-making methods (MCDM). A total of 12 vehicles were analyzed based on 4 main criteria and 12 sub-criteria. The fuzzy Logarithm Methodology of Additive Weights (LMAW) method was applied to find the weights of the main criteria while the fuzzy Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) method was applied to find the weights of the sub-criteria. For the EV ranking, the fuzzy Ranking of Alternatives with Weights of Criterion (RAWEC) method was applied. The findings showed that the most important criteria are the technical criteria and the Auto 11 vehicle showed the best results. The combination of Fuzzy LMAW-Fuzzy LOPCOW-Fuzzy RAWEC methods also constitutes the novelty of this research, which has not been applied before in this field. The contribution of this research consists in providing a comprehensive set of selection criteria to choose the best alternative of the EV fleet in Albania. Furthermore, the contribution of this research was the application of a hybrid methodology in the evaluation and selection of an electric vehicle as an ongoing choice faced by vehicle buyers.

Severe hypoglycemia increases the risk of cardiovascular disease (CVD) in people with diabetes. Large cohort studies and scientific statements show that severe hypoglycemia is linked to higher rates of coronary heart disease, cardiovascular events, and mortality in both type 1 and type 2 diabetes. This risk is especially high in individuals with significant vascular risk, such as older adults and those with multiple cardiovascular risk factors. Hypoglycemia triggers several pathophysiological changes that increase cardiovascular risk. These include activation of the sympathoadrenal system, promotion of proinflammatory and prothrombotic states, arrhythmogenic changes, and increased hemodynamic stress. Experimental evidence shows that recurrent hypoglycemia worsens microvascular dysfunction and promotes adverse cardiac remodeling, especially in people with diabetes. While the link between hypoglycemia and cardiovascular events is well established, the causality remains debated. Hypoglycemia may directly contribute to cardiovascular disease or indicate underlying vulnerability, especially in patients with advanced disease or comorbidities. Minimizing hypoglycemic episodes is recommended for all patients with diabetes, particularly those with established cardiovascular disease, due to the clear association with adverse outcomes.

We address a practical variant of the triangle packing problem: reassembling triangles-originally derived from a Delaunay triangulation of a rectangle-after arbitrary translations and rotations, without overlap, to maximize the covered area. Since triangle packing is an NP-hard problem, we examine four lightweight heuristics that combine translation, rotation, and simple selection rules: (1) grid-guided adjacency, (2) decreasing-area edge joining, (3) random-order edge joining, and (4) length-matching edge joining. Experiments on Delaunaygenerated datasets with 20-60 points show that Strategy 4 achieves the highest average coverage but with greater variance, while Strategy 2 provides the most stable performance. Coverage, runtime, and efficiency metrics demonstrate that even simple geometric heuristics-particularly edge-length matching and edge joining-serve as effective baselines for fast reassembly of triangulated rectangular domains.