Explore the vast range of titles available.

A 38-year-old Caucasian woman, gravida 3 para 2, was admitted at 29 weeks of gestation because of vomiting, dysphagia for solids and liquids, and loss of weight. An enlargement of the anterior left neck region was noted on the palpation of the thyroid gland. An MRI of the neck showed a marked esophageal dilatation with the presence of food remnants along its length and the displacement of the trachea to the right. The findings of the upper gastrointestinal endoscopy and manometry were suggestive of esophageal achalasia. Conservative management with total parenteral nutrition (TPN) through a peripheral line proved to be successful. A healthy male baby was born by a cesarean section at 37 weeks. The patient underwent laparoscopic esophageal myotomy and fundoplication seven days postpartum.

Background We report a unique case of Paget’s disease of vulva and breast. Sequentially the patient had invasive ductal carcinoma of the breast, 5 years later was diagnosed with vulvar Paget’s with underlying adenocarcinoma and after another 2 years was diagnosed with Paget’s disease of the breast. Case A 58-year-old woman with invasive ductal cancer of the left breast was treated with lumpectomy, lymph node dissection, radiation therapy and tamoxifen. Five years later and after complaints of longstanding vulvar pruritus, the patient was diagnosed with vulvar Paget’s disease and treated with simple vulvectomy, which revealed a concurrent underlying adenocarcinoma. Subsequently there was recurrence of vulvar malignancy and wide local excision was performed. Seven years after the initial diagnosis of the breast cancer, a biopsy of a left areolar red, ulcerated lesion revealed Paget’s disease of the breast. Conclusion Physicians need to be cognizant of the rare occurrence of mammary and extramammary Paget’s disease with underlying malignancies in both locations. A thorough physical examination including biopsy is essential for early detection and appropriate management.

Objective We reviewed the available literature on patients with lung cancer undergoing either uniportal (UVATS) or multiport video-assisted thoracoscopic surgery (MVATS). Methods Original research studies that evaluated perioperative and long-term outcomes of UVATS versus MVATS were identified, from January 1990 to April 2020. The perioperative, along with the oncologic and long-term survival outcomes, were calculated according to either a fixed or a random effect model, appropriately. The Q statistics and I 2 statistic were used to test for heterogeneity among the studies. Results Twenty studies were included, incorporating a total of 1,469 patients treated with UVATS and 3,231 treated with MVATS. The incidence of complications was lower in patients treated with UVATS [OR: 0.76 (95% CI 0.62, 0.93); p  = 0.008]. The chest tube duration was significantly lower in the UVATS group (WMD: − 0.63 [95% CI − 1.03, − 0.23]; p = 0.002). Length of hospital stay (L.O.S.) was also lower in the UVATS patient group (WMD: − 0.54 [− 0.94, − 0.13]; p = 0.009), along with postoperative pain [WMD: − 0.57 (95% CI − 0.97, − 0.18); p  = 0.004]. No significant differences were found regarding the mean operative time (M.O.T.), mean blood loss, the number of resected lymph nodes, the 30-day mortality, along with the survival at 1 and 3 years postoperatively. Conclusions The present meta-analysis indicates that UVATS is associated with enhanced outcomes in patients undergoing surgery for lung cancer. Well-designed, randomized studies, comparing UVATS to MVATS, are necessary to further assess their long-term clinical outcomes.

Background/Objectives: SARS-CoV-2 can affect the central nervous system directly or indirectly. AD shares several similarities with long COVID cognitive impairment on a molecular and imaging level, as well as common risk factors. The objective of this review is to evaluate the incidence of post-acute COVID-19 cognitive impairment. Secondarily, we aim to determine if neuroinflammation in COVID-19 survivors may be associated with the onset of neurological disease, with a focus on Alzheimer’s disease (AD). Methods: literature search up to March 2025 on the prevalence of cognitive deficits in COVID-19 survivors, underlying pathophysiology and associations with neurological disorders. Results: a wide array of neuropsychiatric manifestations is associated with COVID-19; executive function, memory, and attention are the most frequently reported neurocognitive deficits, regardless of COVID-19 severity. There are associations between the risks for cognitive deficits post-infection with the age of the patients and the severity of the disease. Increasing evidence suggests that neurocognitive deficits are associated with the onset of neurological and neuropsychiatric disease in COVID-19 survivors. Conclusions: clinicians caring for COVID-19 survivors should actively investigate neurocognitive sequelae, particularly for patients with increased risk for cognitive deficits.

Several modern day problems need to deal with large amounts of spatio-temporal data. As such, in order to meet the application requirements, more and more systems are adapting to the specificities of those data. The most prominent case is perhaps the data storage systems, that have developed a large number of functionalities to efficiently support spatio-temporal data operations. This work is motivated by the question of which of those data storage systems is better suited to address the needs of industrial applications. In particular, the work conducted, set to identify the most efficient data store system in terms of response times, comparing two of the most representative of the two categories (NoSQL and relational), i.e. MongoDB and PostgreSQL. The evaluation is based upon real, business scenarios and their subsequent queries as well as their underlying infrastructures and concludes in confirming the superiority of PostgreSQL in almost all cases with the exception of the polygon intersection queries. Furthermore, the average response time is radically reduced with the use of indexes, especially in the case of MongoDB.

The overall aim of this work is to perform a systematic analysis of several off-the-shelf machine learning classification algorithms and to assess their ability to classify Search And Rescue (SAR) patterns from noisy Automatic Identification System (AIS) data. Specifically, we evaluate Decision Trees, Random Forests and Gradient Boosted Trees on a large volume of historical AIS data so as to detect SAR activity from vessel trajectories, in a scalable, data-driven supervised way, with no reliance on external sources of information (e.g. coast guard reports). Our analysis verifies that it is possible to identify SAR patterns, while the results show that although all algorithms are capable of achieving high accuracy, Random Forests marginally outperform the others in terms of performance and speed of execution.

A thorough literature search was conducted on patients with Left Ventricular Assist Device (LVAD) and aortic insufficiency undergoing transcatheter aortic valve replacement (TAVR). We identified all original research studies that compared the long-term outcomes of surgical transcatheter aortic valve replacement (SAVR) versus TAVR for patients with LVAD, published between 1990 and 2023. The primary endpoint was the composite of in-hospital mortality, stroke, transient ischemic attack, myocardial infarction (MI), pacemaker implantation, vascular complications and cardiac tamponade. Secondary endpoints were the median overall survival (OS), the incidence of acute kidney injury (AKI), any bleeding needing transfusions or reintervention and cost. A total of fourteen studies and 358 patients were included (TAVR: 242; SAVR: 116). The composite outcome, the incidence of AKI, the bleeding needing transfusion, along with cost were significantly higher in the SAVR group. In addition, there was no significant difference between TAVR and SAVR in terms of median OS. The median OS in the TAVR group was 18 months. Finally, the most common causes of death were progression of heart failure and pneumonia. The present meta-analysis indicates that TAVR is associated with enhanced outcomes compared to SAVR for patients with LVAD presenting aortic insufficiency. Further well-designed original studies with greater sample sizes are necessary to validate our findings.

Objective We reviewed the available literature on patients with coronary artery disease undergoing isolated coronary artery bypass grafting (CABG) with either single (SAG) or multiple arterial grafting (MAG). Methods Original research studies that evaluated the long-term survival of MAG versus SAG were identified, from 1995 to 2022. The median overall survival (OS) and event-free OS were the primary endpoints. Comparison of median OS between the right internal mammary artery (RIMA) and radial artery (RA) as a second arterial conduit was the secondary endpoint. Subgroup analyses were performed regarding patients older than 70 years, with diabetes mellitus, and females. A sensitivity analysis was performed with the leave-one-out method. Results Forty-four studies were included in the qualitative and thirty-nine in the quantitative synthesis. After pooling data from 180 to 459 patients, the MAG group demonstrated a higher OS (HR, 0.589; 95% CI, 0.58–0.60; p  < 0.0001) and event-free OS compared with the SAG group (HR, 0.828; 95% CI, 0.80–0.86; p  < 0.0001). In addition, RITA was associated with superior OS compared with RA as a second arterial conduit (HR, 0.936; 95% CI, 0.89–0.98; p  = 0.009). MAG was also superior to SAG in patients over 70 years, females, and patients with diabetes mellitus. Sensitivity analysis demonstrated a small-size study effect on the female subgroup analysis. Conclusion The present meta-analysis indicates that MAG is associated with enhanced survival outcomes compared to SAG for patients undergoing isolated CABG.

In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the “garbage in, garbage out” issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians’ hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.