Microsoft (Brazil)

Medical cases involving foreign bodies can present unique challenges, especially when they occur in sensitive areas, such as the neovagina of a male-to-female transgender. A 32-year-old transgender woman attended a tertiary, academic-affiliated hospital and reported to insert a vaginal dilator into the neovagina after removing its base and after mild abdominal pain and difficulty in removing the dilator after multiple successful attempts, she looked for help in the emergency room. A video article demonstrates a step-by-step approach to remove the device. Under intravenous anesthesia, a neovaginoscopy was performed using a saline solution for dilation with a Bettocchi Hysteroscopy Set, equipped with a 4mm and 30º scope, ultimately revealing the absence of fistulas or perforations. Hydrodistension probably played a major role to reduce difficulty to remove the dilator, as this was clamped with Pozzi forceps after 800ml of infusion. Urogenital fistulas or rupture of the neovaginal vault was excluded after careful revision. Patient was discharged on the same day with a prescription for antibiotics. Neovaginoscopy with an important hydrodistention proved to be a method for locating and removing the entrapped dilator.

Hormone therapy is widely used for contraception, disease treatment, and as hormone replacement therapy in women. Many studies have attempted to establish the relationship between the use of exogenous hormones and the development of CNS tumors; however, the results remain inconclusive. The objective of this meta-analysis is to evaluate the association between hormone therapy use and the risk of developing central nervous system (CNS) tumors, particularly meningioma and glioma. PubMed, Scopus, and Web of Science databases were searched for studies reporting the association between hormone therapy use and CNS tumor risk. The outcomes analyzed were overall risk for CNS tumors, meningioma, and glioma, described as odds ratios (ORs) and hazard ratios (HRs). Statistical analyses were performed using R (version 4.4.2), with a significance level of p < 0.05. The analysis of the 19 studies involving 2.398.681 patients demonstrated a significant association between hormone therapy use and the overall risk of CNS tumors (OR = 1.69; 95% CI, 1.28–2.23; p < 0.001; I² = 98.5%). Subgroup analysis revealed an increased risk for the development of meningioma (OR = 2.10; 95% CI, 1.52–2.91; I² = 98.6%), whereas for gliomas the exposure may not be associated with an increased risk (OR = 0.90; 95% CI, 0.64–1.27; I² = 84.9%). The overall risk of CNS tumors was also expressed using hazard ratios (HR = 1.18; 95% CI, 0.97–1.45; p = 0.097; I² = 71.0%), showing an insignificant trend favoring non-use of hormone therapy, with an increased risk observed for meningioma (HR = 1.59; 95% CI, 1.09–2.32; I² = 72.8%), while based on the analyzed data, hormone therapy use may not appear to increase the risk of glioma development (HR = 0.99; 95% CI, 0.86–1.16; I² = 36%). This meta-analysis suggests that exogenous hormonal therapy is associated with an increased risk of central nervous system tumors in women, driven predominantly by meningioma risk. No consistent association was observed for glioma.

This study aimed to evaluate the coherence between data heterogeneity and model complexity by comparing seven convolutional neural network (CNN) architectures-trained with and without ImageNet pretraining-in a multiclass framework for the histopathological classification of three odontogenic tumors: adenomatoid odontogenic tumor, ameloblastoma, and ameloblastic carcinoma. The goal was to investigate how transfer learning influences performance and diagnostic reliability in a clinically relevant context characterized by overlapping histological patterns. An international, multicenter cross-sectional dataset of 64 hematoxylin- and eosin-stained whole slide images was analyzed, including adenomatoid odontogenic tumor (n = 16), ameloblastoma (n = 27), and ameloblastic carcinoma (n = 21). Seven CNN models (DenseNet121, EfficientNetV2B0, InceptionV3, MobileNet, ResNet50, VGG16, and Xception) were trained and tested on 455,107 patches (224 × 224 pixels). Performance was assessed using accuracy, balanced accuracy, sensitivity, specificity, F1-score, and AUC. Without ImageNet pretraining, DenseNet121 achieved the highest performance (accuracy = 0.73, balanced accuracy = 0.74, AUC = 0.78, specificity = 0.84, sensitivity = 0.65), followed by EfficientNetV2B0 (accuracy = 0.67, balanced accuracy = 0.68, sensitivity = 0.54). When ImageNet pretraining was applied, performance improved across all architectures. EfficientNetV2B0 reached the best overall results (accuracy = 0.79, balanced accuracy = 0.81, AUC = 0.91, specificity = 0.88, sensitivity = 0.74), while DenseNet121 maintained consistent performance (accuracy = 0.72, balanced accuracy = 0.74, AUC = 0.85, specificity = 0.84, sensitivity = 0.64). Transfer learning with ImageNet weights enhanced the performance of most CNNs, with EfficientNetV2B0 showing the greatest responsiveness to pretraining and DenseNet121 demonstrating intrinsic robustness to initialization. These results highlight the potential of CNN-based frameworks to support the differential diagnosis of odontogenic tumors-an inherently challenging task due to morphological overlap-while establishing reproducible methodological baselines that contribute to the global development of explainable, ensemble-based, and clinically reliable AI systems in oral pathology.

BackgroundAPOE‐ε4 carriers and individuals with unfavorable social determinants of health (SDH) profiles have an increased risk for Alzheimer's Disease (AD). However, the modification effect of SDH on the associations of APOE‐ε4 with AD pathology or AD pathology with AD dementia symptoms is yet to be understood, particularly in low‐ to middle‐income countries where social disparities play an important role in AD burden and might interact with genetic variants.MethodThis cross‐sectional study used data from the Biobank for Aging Studies. Individuals aged 50 years or older and whose next of kin (NOK) had at least a weekly contact with the deceased were included. Other causes of dementia besides AD pathology were excluded (n = 196). Individuals were classified into APOE‐ε4 carriers (at least one ε4 allele) and non‐carriers. The individual's NOK provided information on SDH. AD neuropathological changes (ADNC) were evaluated following international criteria using the CERAD and Braak stagings. Clinical Dementia Rating ‐ Sum of Boxes (CDR‐SB) was used to assess the extent of AD symptoms. Confirmatory factor analysis was conducted to create an SDH general factor. Logistic and linear regressions were used to investigate the associations of APOE‐ε4 with AD pathology and AD pathology with AD symptoms, respectively. Interactions of the SDH factor with APOE‐ε4 and AD pathology were tested for both associations. SDH‐profile‐stratified (favorable vs unfavorable SDH profiles based on the SDH factor's median) analyses were conducted for significant interactions.ResultIn 1,021 individuals, the mean (SD) age was 74.2(12.6) years old, 51.2% were women, and 35.1% were Black/Brown. The association of APOE‐ε4 genotype with AD pathology was not modified by the SDH factor (p = 0.264), while the association of AD pathology with AD symptoms was modified by the SDH factor (p = 0.001). In stratified analysis, the association of AD pathology with AD symptoms was stronger in the unfavorable (β=8.42; 95%CI=7.44, 9.39; p <0.001) compared to the favorable SDH group (β=7.90; 95%CI=7.04, 8.77; p <0.001).ConclusionSDH did not modify the association between APOE‐ε4 and AD pathology, while it modified the expression of dementia in participants with AD pathology. This association was stronger in individuals with unfavorable compared to favorable SDH profile.