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Introduction: Oral cancer (OC) is a disease with a high mortality rate due to its late recognition. Since the oral cavity is easily accessible for visual inspection, enabling early diagnosis, the education of healthcare workers about preventive oral examinations is critical. This research aimed to assess the level of participants’ OC knowledge, as well as to raise awareness about this diagnosis. Materials and methods: The research was conducted as an online survey among students of dental medicine, students of medicine, doctors of dental medicine and doctors of medicine. The questionnaire was designed solely for the purpose of this study and consisted of 29 questions. The first part of the questionnaire consisted of general questions about the participants, whereas the questions in the second part addressed their knowledge and attitudes towards OC. Results: The surveyed population comprised of 140 dental students, 105 medical students, 159 doctors of dental medicine and 100 medical doctors. The level of knowledge about OC among the participants is not yet satisfactory. The group of dental medicine students scored highest, while medical doctors showed the weakest knowledge. Conclusion: Additional education about OC for doctors of dental medicine and medical doctors is needed. This step will improve prevention and increase chances for early detection.

PurposeTo investigate the value of T2-radiomics combined with anatomical MRI staging criteria from pre-treatment rectal MRI in predicting complete response to neoadjuvant chemoradiation therapy (CRT).MethodsThis retrospective study included patients with locally advanced rectal cancer who underwent rectal MRI before neoadjuvant CRT from October 2011 to January 2015 and then surgery. Surgical histopathologic analysis was used as the reference standard for pathologic complete response. Anatomical MRI staging criteria were extracted from our institutional standardized radiology report. In radiomics analysis, one radiologist manually segmented the primary tumor on T2-weighted images for all 102 patients (i.e., training set); two different radiologists independently segmented 66/102 patients (i.e., validation set). 108 radiomics features were extracted. Then, scanner-independent features were identified and least absolute shrinkage operator analysis was used to extract a radiomics score. Finally, a support vector machine model combining the radiomics score and anatomical MRI staging criteria was compared against both anatomical MRI-only and radiomics-only models using the deLong test.ResultsThe study included 102 patients (42 women; median age = 61 years).The radiomics score produced an area under the curve (AUC) of 0.75. Comparable results were found using the validation set (AUCs = 0.75 and 0.71 for each radiologist, respectively). The anatomical MRI-only model had an accuracy of 67% (sensitivity 42%, specificity 72%); when adding the radiomics score, the accuracy increased to 74% (sensitivity 58%, specificity 77%).ConclusionCombining T2-radiomics and anatomical MRI staging criteria from pre-treatment rectal MRI may help to stratify patients based on the prediction of treatment response to neoadjuvant therapy.

Osteonecrosis of the jaw is a condition in which bone cells die due to various causes. It is classified as drug-induced jaw osteonecrosis, osteoradionecrosis, traumatic, non-traumatic, and spontaneous osteonecrosis. Antiresorptive or antiangiogenic drugs cause drug-induced osteonecrosis. The combination of medications, microbial contamination, and local trauma induces this condition. Osteoradionecrosis is a severe radiation therapy side effect that can affect people with head and neck cancer. It is described as an exposed bone area that does not heal for longer than three months after the end of radiation treatment with the absence of any indications of an original tumor, recurrence, or metastasis. Trauma (tooth extraction), tumor site, radiation dose that the patient receives, the area of the bone which is irradiated, oral hygiene, and other factors are risk factors for the development of osteonecrosis. Less frequently, osteonecrosis can also be induced by non-traumatic and traumatic causes. Non-traumatic osteonecrosis is brought on by infections, acquired and congenital disorders, as well as the impact of chemicals. Traumatic osteonecrosis is brought on by thermal, mechanical, or chemical damage. The treatment of osteonecrosis can be conservative, which aims to be beneficial for the patient’s quality of life, and surgical, which involves debridement of the necrotic bone.

IntroductionAs computational capabilities have advanced, radiologists and their collaborators have looked for novel ways to analyze diagnostic images. This has resulted in the development of radiomics and radiogenomics as new fields in medical imaging. Radiomics and radiogenomics may change the practice of medicine, particularly for patients with colorectal cancer. Radiomics corresponds to the extraction and analysis of numerous quantitative imaging features from conventional imaging modalities in correlation with several endpoints, including the prediction of pathology, genomics, therapeutic response, and clinical outcome. In radiogenomics, qualitative and/or quantitative imaging features are extracted and correlated with genetic profiles of the imaged tissue. Thus far, several studies have evaluated the use of radiomics and radiogenomics in patients with colorectal cancer; however, there are challenges to be overcome before its routine implementation including challenges related to sample size, model design and interpretability, and the lack of robust multicenter validation set.Material and methodsIn this article, we will review the concepts of radiomics and radiogenomics and their potential applications in rectal cancer.ConclusionRadiologists should be aware of the basic concepts, benefits, pitfalls, and limitations of new radiomic and radiogenomics techniques to achieve a balanced interpretation of the results.

The increasing integration of large language models (LLMs) into software engineering workflows under the term vibe-coding necessitates systematic empirical evaluation of their code generation capabilities, especially in the context of complex backend development and architectural decision-making. This study compares popular foundational models Google Gemini 3 Pro and DeepSeek-V3.1 for developing a Java/Spring Boot backend application using a structured prompt-chaining protocol following a typical vibe-coding process. The generated solutions were evaluated using several quantitative and qualitative criteria, including the number of corrective prompts, the extent of required manual code interventions, functional correctness, architectural robustness, maintainability-related design choices, latency, and test quality. The results show substantial differences between the two models. DeepSeek required twice as many corrective natural language prompts as Gemini, but both models required a similar number of manual interventions in the generated code, with 23 for DeepSeek and 20 for Gemini. The most pronounced difference was in architectural reasoning. Gemini autonomously introduced the Data Transfer Object design pattern, resulting in a decoupled architecture, although at a cost of a minor performance issue. In contrast, DeepSeek was better in development of boilerplate code but exposed raw JPA entities through the application interface leading to tight coupling and other issues. Gemini’s solution satisfied 90.25% of evaluated requirements compared to 68.08% for DeepSeek. Additionally, generated tests showed a higher success rate and broader code coverage, achieving 95.7% successful test execution and 55.9% code coverage for Gemini, compared to 74.1% and 45.6% for DeepSeek, respectively. The results indicate that within the paradigm of vibe-coding, even the best available foundational LLMs may still require expert human supervision, especially when the generated code is expected to satisfy specific requirements in production-oriented backend systems.

Rectal adenocarcinoma with mucinous components is an uncommon type of rectal cancer with two distinct histologic subtypes: mucinous adenocarcinoma and signet-ring cell carcinoma. Mucin can also be identified as pattern of response after neoadjuvant treatment. On imaging modalities, mucin typically demonstrates high signal intensity on T2-weighted images, low attenuation on computed tomography, and may be negative on 18-fluorodeoxyglucose positron emission tomography. After neoadjuvant CRT, cellular and acellular mucin share similar imaging features, and differentiating them is currently the main challenge faced by radiologists. Radiologists should be aware of pros, cons, and limitations of each imaging modality in the primary staging and restaging to avoid misinterpretation of the radiological findings.

Shallots are a perennial plant from the Alliaceae family, classified with the common onion under the name of the Allium cepa Aggregatum group. The term shallot is also used for diploid and triploid viviparous onions, known as Allium × proliferum (Moench) Schrad and Allium × cornutum Clementi ex Vis., respectively. In this study, we compared the dry matter, pyruvic acid content, sugar content, flavonoid content, antioxidant capacity and mineral composition of 34 shallot accessions falling into three shallot species (Allium × cornutum, Allium × proliferum and A. cepa Aggregatum). Shallot accessions belonging to the A.× cornutum and A. × proliferum groups are characterized by high dry matter content (around 25%), of which a little less than 50% is formed of inulin-type sugars, polysaccharides, considered an excellent prebiotic with beneficial effects on human health. On the other hand, accessions belonging to the A. cepa Aggregatum group have lower dry matter content and, as a result, lower pungency (measured as pyruvic acid content), making them more suitable for fresh consumption by a broader range of consumers, but, at the same time, abundant in phenolic compounds, especially quercetin and isorhamnetin glycosides. We also observed a greater biodiversity among accessions within the A. cepa Aggregatum group in all the analyzed physico-chemical parameters compared to the other shallot groups. The investigated shallot accessions have an excellent in vitro antioxidant capacity, as well as excellent nutritional properties.

Relapse remains the leading cause of treatment failure after allogeneic hematopoietic stem cell transplantation (allo-HSCT) in acute myeloid leukemia (AML). Early identification of patients at increased post-transplant relapse risk is essential to enable intensified surveillance and pre-emptive therapeutic strategies. Wilms’ tumor 1 (WT1) is overexpressed in most AML cases and represents a broadly applicable molecular marker; however, its utility as a peripheral blood (PB) measurable residual disease (MRD) marker after allo-HSCT remains incompletely defined. In this prospective multicenter cohort study, 43 adults with AML in complete remission underwent allo-HSCT between 2021 and 2023. WT1 expression in PB was quantified using standardized real-time quantitative PCR before transplantation (WT1_pre) and at day +30 (WT1_30). Receiver operating characteristic analysis identified an optimal threshold for relapse prediction. A WT1 cutoff of ≥3 copies/104 ABL discriminated relapse risk. WT1_30 demonstrated strong prognostic performance (AUC 0.79; p = 0.005), whereas WT1_pre showed more modest predictive value (AUC 0.69; p = 0.037). Patients with WT1_30 ≥ 3 had inferior 12-month progression-free survival compared with those with WT1_30 < 3 (52.9% vs. 90.9%, p = 0.0059) and a higher 12-month cumulative incidence of relapse (31% vs. 9%, p = 0.054). WT1_pre ≥ 3 was also associated with inferior progression-free and overall survival (both p = 0.0008). Relapsed patients had significantly higher WT1_30 levels than non-relapsed patients (median 5.0 vs. 2.0 copies/104 ABL; p = 0.018). Peripheral blood WT1 expression, particularly at day +30, is associated with an increased relapse risk after allo-HSCT in AML and may support early post-transplant risk stratification. The identified cutoff should be considered exploratory and requires validation in larger independent cohorts.