Alexandra Hospital

Hexavalent chromium (Cr(VI)) is a highly toxic and carcinogenic trace element. While carcinogenicity through inhalation is well-established, gastrointestinal (GI) carcinogenicity via oral ingestion remains contentious. This study aimed to measure GI cancer mortality at Oinofyta, Greece, where the toxic waste of industries was discarded in the village’s water source for a long time. An ecological study was carried out at the Oinofyta municipal unit, where the primary water supply had been contaminated with Cr(VI) for approximately three decades. Mortality data of all residents of Oinofyta for the period 2000–2021 were obtained from the Hellenic Statistical Authority, and causes of death were classified according to the ICD-10. Standardized Mortality Ratios (SMRs) were computed, stratified by five-year age groups, biological sex, and calendar year, using the population of the entire Voiotia regional unit as the reference population. A higher all GI cancer SMR was observed during the second decade (SMR = 1.44; 95%CI = 1.03, 1.95), but not the first (SMR = 0.98; 95%CI = 0.64, 1.44). Overall, the SMR was evidently higher for males (SMR = 1.35; 95%CI = 1.0, 1.8), but not for females (SMR = 0.97; 95%CI = 0.6, 1.49). A borderline higher SMR was also observed for colorectal cancer in males (SMR = 1.63; 95%CI = 0.93, 2.65; p = 0.08). Additionally, the SMR for all GI cancers demonstrated a significant increasing trend from 2000–2009 to 2010–2021 (0.98; 95%CI = 0.64, 1.44 to 1.44; 95%CI = 1.03, 1.95). This ecological study presents a population-level association between Cr(VI)-contaminated drinking water with certain GI cancers, suggesting further research for etiological associations.

В работе рассматривается актуальная задача обнаружения сверхмалых объектов в условиях железнодорожной инфраструктуры для повышения безопасности движения. Традиционные методы объектного детектирования на основе сверточных нейронных сетей сталкиваются с трудностями при обнаружении объектов на больших расстояниях из-за сильной деградации изображения, что приводит к снижению их эффективности. В связи с этим разработка эффективных методов детекции сверхмалых объектов является критически важной. Предлагается новый подход, основанный на анализе низкоуровневых радиометрических сигналов DRI. Он опирается на извлечение и анализ статистических характеристик яркости, сохраняющихся даже при значительной пространственной деградации. Даже при ограниченной визуальной информации предложенный метод демонстрирует большую точность обнаружения: 70–75 % для сверхмалых объектов размером менее 20 пикселей. This paper addresses the current challenge of detecting ultra-small objects within railway infrastructure to enhance traffic safety. Traditional object detection methods based on convolutional neural networks encounter difficulties in detecting distant objects due to severe image degradation, leading to reduced effectiveness. Consequently, developing effective methods for ultra-small object detection is critically important. This work proposes a novel approach based on the analysis of low-level radiometric DRI signals. It relies on extracting and analyzing statistical brightness characteristics that are preserved even with significant spatial degradation. Even with limited visual information, the proposed method demonstrates high detection accuracy: 70-75% for ultra-small objects less than 20 pixels in size.

Nowadays, adipose tissue is recognized as a dynamic endocrine organ that profoundly affects metabolic, reproductive and skeletal physiology. Apart from its role in energy storage, adipocytes secrete cytokines and bioactive proteins, known as adipokines, that contribute to the regulation of the hypothalamic-pituitary-gonadal axis, gonadal steroidogenesis and bone remodeling. Reciprocal signaling between adipose tissue, bone and the reproductive system forms a complex endocrine network linking energy balance to fertility and bone homeostasis. Key mediators, including leptin, adiponectin, resistin, visfatin and bone marrow adipose tissue, interact with osteoblast-derived proteins, such as osteocalcin, to modulate steroidogenesis and metabolic function. Vitamin D, stored in adipose tissue and acting through its receptor, further integrates these pathways by coordinating calcium metabolism, reproduction and skeletal integrity. This review aims to present mechanistic and clinical evidence demonstrating how metabolic signals derived from adipose depots communicate with gonadal and skeletal tissues through overlapping endocrine, paracrine and autocrine networks. Attention is paid to depot-specific effects, bidirectional feedback between adipose tissue and bone signaling, as well as in emerging implications for infertility, osteoporosis and metabolic bone disease. Finally, we discuss critical knowledge gaps and research priorities, to elucidate novel therapeutic approaches for reproductive and skeletal disorders.

Background/Objectives: Accurate preoperative characterization of ovarian masses is essential for appropriate clinical management, particularly for borderline ovarian tumors (BOTs), which are less common and often difficult to distinguish from benign or malignant lesions on ultrasound. Although expert subjective ultrasound assessment achieves high diagnostic accuracy, limited availability of highly trained sonologists restricts its widespread application. Artificial intelligence-based approaches offer a potential solution; however, the low prevalence of BOTs restricts the development of robust deep learning models due to severe class imbalance. This study aimed to develop a Convolutional Neural Network (CNN)-based classifier enhanced with Generative Adversarial Networks (GANs) to improve the discrimination of ovarian masses as benign, malignant, or BOT using ultrasound images. Methods: A total of 3816 ultrasound images from 636 ovarian masses were retrospectively analyzed, including 390 benign lesions, 202 malignant tumors, and 44 BOTs. To address class imbalance, a Deep Convolutional GAN (DCGAN) was used to generate 2000 synthetic BOT images for data augmentation. A three-class ensemble CNN model integrating VGG16, ResNet50, and InceptionNetV3 architectures was developed. Performance was assessed on an independent test set and compared with a baseline model trained without DCGAN augmentation. Results: The incorporation of DCGAN-generated BOT images significantly enhanced classification performance. The BOT F1-score increased from 68.4% to 86.5%, while overall accuracy improved from 84.7% to 91.5%. For BOT identification, the final model achieved a sensitivity of 88.2% and specificity of 85.1%. Class-specific AUCs were 0.96 for benign lesions, 0.94 for malignant tumors, and 0.91 for BOTs. Conclusions: DCGAN-based augmentation effectively expands limited ultrasound datasets and improves CNN performance, particularly for BOT detection. This approach demonstrates potential as a decision support tool for preoperative assessment of ovarian masses.

Gynecologic cancer is a major global health burden, and improvements in screening, surgical techniques, and systemic therapies have significantly prolonged survival. As a result, cardiopulmonary disease has emerged as a leading non-cancer cause of morbidity and mortality among gynecologic cancer survivors. Obesity, which is highly prevalent in this population, substantially increases cardiopulmonary risk by contributing to metabolic syndrome, cardiovascular disease, chronic inflammation, and reduced cardiopulmonary reserve. This narrative review summarizes current evidence on the epidemiology, pathophysiological mechanisms, and clinical spectrum of cardiopulmonary complications in obese patients with gynecologic malignancy. We review the contribution of obesity-related metabolic and endothelial dysfunction, cancer-associated hypercoagulability, and treatment-related toxicities, with particular emphasis on complications arising from surgery, chemotherapy, and radiotherapy. Epidemiologic data demonstrate a markedly increased burden of cardiovascular and pulmonary disease in obese gynecologic cancer patients, including higher rates of myocardial injury, heart failure, venous thromboembolism, and postoperative respiratory complications. Surgical treatment, although central to oncologic management, imposes substantial cardiopulmonary stress, placing obese patients at heightened perioperative risk. Future studies should focus on preoperative risk stratification, optimization of obesity-related comorbidities, and multidisciplinary perioperative management, including enhanced recovery pathways, as well as appropriate use of high-dependency or intensive care monitoring to reduce morbidity and improve both oncologic and long-term non-oncologic outcomes in this population.

Background and Objectives: Tubal reanastomosis is an alternative option for women seeking fertility after sterilization. Thus, anastomosis healing quality and peri-tubal adhesions play a crucial role. TISSEEL® fibrin sealant may enhance tissue repair and reduce foreign-body reaction. We evaluated the effect of TISSEEL®, used alone or with sutures, on anastomotic healing and adhesion formation in a rat uterine horn model. Materials and Methods: Thirty female Wistar rats were randomized to Suture, TISSEEL®, or Suture + TISSEEL® groups (n = 10 each). After bilateral uterine horn transection, reanastomosis was performed with sutures alone, fibrin sealant alone, or combined sutures and sealant. On postoperative day 14, reanastomosis segments were collected for blinded histologic assessment and evaluation of modified Ehrlich-Hunt score parameters (inflammation, fibrosis, neovascularization and collagen production). Intra-abdominal adhesions were also macroscopically assessed. Results: Two animals died perioperatively and 56 uterine horns were included in the final analysis (Suture n = 18, TISSEEL®n = 18, Suture + TISSEEL®n = 20). The distribution of inflammation and fibrosis severity grades, as assessed by the modified Ehrlich-Hunt scoring system, did not differ significantly between the study groups (p = 0.208 and p = 0.652, respectively). In contrast, high-grade neovascularization (grades 3-4) was more common in TISSEEL® groups (77.8% TISSEEL®, 80.0% Suture + TISSEEL®, 33.3% Suture, p = 0.004), while increased collagen deposition was also more common in the TISSEEL® groups (p = 0.011), after binary analysis. Severe adhesions were more common in the Suture group (66.7% vs. 11.1% in the TISSEEL® group and 30.0% in the Suture + TISSEEL® group, p = 0.037). Conclusions: TISSEEL®, alone or as an adjunct to sutures, improves neovascularization and collagen production and is associated with milder adhesions without increased inflammation or fibrosis. The use of fibrin sealant TISSEEL® may be a useful tool in tubal reconstructive surgery.

The discovery of antibody-drug conjugates (ADCs) has revolutionized the therapeutic landscape of oncology patients, especially those suffering from breast cancer. Following the approval of the first ADC for solid tumors in breast cancer, numerous additional ADCs have also been launched in the therapeutic landscape of breast cancer and have become the new standard of care for all diverse subtypes. Sacituzumab tirumotecan (sac-TMT) (MK-2870/SKB264) is an innovative ADC targeting TROP2 and delivering a belotecan-derived topoisomerase I inhibitor payload. Several clinical trials of sac-TMT have demonstrated promising results including improved overall response and disease control rates as well as progression-free survival. The safety profile of sac-TMT seems easily manageable; adverse events include mainly grade 1/2 nausea and alopecia, while grade 3/4 neutropenia and leukopenia and grade 3/4 stomatitis have been also reported. However, no treatment-related deaths have been reported so far. Since sac-TMT offers an encouraging new option for diverse breast cancer populations with manageable toxicity profiles, this review aims to summarize the recent published data regarding its use in breast cancer.

Objective.Establishing control and specification limits for volumetric modulated arc therapy (VMAT) pre-treatment quality assurance (PTQA) is essential for streamlining PTQA workflows and optimizing plan complexity. This study aimed to develop and implement new methods and tools across treatment sites of varying complexity using multiple global and local gamma index criteria.Approach.350 VMAT plans comprising brain, prostate, pelvis and head and neck treatments were retrospectively compiled. For each site, control limits were obtained using statistical process control (SPC) along with heuristic methods (scaled weighted variance (SWV), weighted standard deviation (WSD), skewness correction (SC)). Specification limits were derived employing a new formalism aligned with the heuristic approaches. Calculations were performed under various global and local gamma index criteria using custom-built software (freely available athttps://github.com/AEvgeneia/SPC_GUI_Scientific_Tool.git).Main results.WSD and SC control and specification limits were comparable, while SWV deviated with increasing complexity and stricter gamma index criteria. Conventional criteria (e.g. global 3%/2 mm) lacked sensitivity to detect subtle errors. Global 2%/1 mm and 1%/2 mm, and local criteria stricter than 3%/1 mm, met sensitivity requirements for low-complexity plans while maintaining clear separation between control and specification limits to identify plans with suboptimal delivery accuracy. High-complexity plans showed that global criteria stricter than 3%/1 mm and all evaluated local criteria are optimal, provided specification limits for the most stringent criteria remain clinically acceptable.Significance.A nuanced framework is provided for determining control and specification limits for gamma index passing rates, as well as corresponding thresholds for the mean gamma index, allowing for site-specific detection of suboptimal treatment plans. The open-source software tool developed can operationalize the proposed methodology facilitating the clinical adoption of advanced statistical methods. Site-specific thresholds could serve as inputs for machine learning and deep learning algorithms aimed at automating error detection and PTQA classification for plan complexity management.