Complex decision making in a patient with lung cancer with incidentally found fast-growing atrial mass.

Atrial myxomas are typically found in the left atrium and are the most common among overall rare cardiac tumors. Herein, we describe the clinical course of a 72-year-old female with non-small cell lung adenocarcinoma found to have an atrial mass during an imaging for evaluation for lung cancer progression. Differentiating between distinct types of masses can pose a challenge to the treatment team especially in the setting of exiting malignancy. This case demonstrates the complex decision making involved in the diagnosis, and timing of intervention to remove atrial mass in patients with frailty and a fast-growing cardiac mass.

Late-stage diagnosis of carcinoid heart disease due to lack of access to health care.

Carcinoid syndrome (CS) is a unique constellation of symptoms caused by release of vasoactive substances from neuroendocrine tumors (Pandit et al., StatPearls, 2022). Neuroendocrine tumors are rare with an annual incidence of 2 in 100,000 people (Ram et al., 46:21-27, 2019). Up to 50% of patients with these tumors will develop carcinoid syndrome, which is characterized by symptoms caused by elevated levels of serotonin and most commonly include fatigue, flushing, wheezing, and non-specific gastrointestinal symptoms such as diarrhea and malabsorption (Pandit et al., StatPearls, 2022) (Fox et.al., 90:1224-1228, 2004). Over time, patients with carcinoid syndrome can develop carcinoid heart disease (CHD). CHD refers to the cardiac complications that occur when the vasoactive substances, such as serotonin, tachykinins, and prostaglandins, secreted from the carcinoid tumors. These complications most commonly include valvular abnormalities, but can also present as coronary artery damage, arrhythmias or direct myocardial injury (Ram et al., 46:21-27, 2019). While CHD is not typically an initial feature of carcinoid syndrome, it does eventually occur in up to 70% of patients with carcinoid tumors (Ram et al., 46:21-27, 2019) (Jin et.al., 146:65-73, 2021) (Macfie et.al., 224:665-669, 2022). CHD is associated with significant morbidity and mortality due to the risk of progressive heart failure (Bober et.al., 14:1179546820968101, 2020). In this case, we describe a 35-year-old Hispanic woman in South Texas with undiagnosed carcinoid syndrome for over 10 years that eventually progressed to severe CHD. In this patient's case, we emphasize how lack of access to healthcare resulted in delay of diagnosis, appropriate treatment, and worsened prognosis in this young patient.

Long-Term Complications of COVID-19 Infection in Adolescents and Children.

Purpose of Review: Compared to adults, post-COVID-19 symptoms are uncommon and have not been thoroughly evaluated in children. This review summarizes the literature in terms of persistent symptoms in children and adolescents after SARS-CoV-2 infection. Recent Findings: Children were less likely to develop long COVID when compared to adults. Older children (e.g., adolescents) and those who had symptomatic COVID-19 had a higher probability for long COVID. Summary: Families and health care providers need to be aware of a new constellation of long COVID symptoms in the pediatric population. More evidence and time are needed to better understand the potential effects of long COVID-19 in children and adolescents. In comparison to adults, children are less likely to have persistent COVID-19 symptoms.

Insights into Mechanisms of Pheochromocytomas and Paragangliomas Driven by Known or New Genetic Drivers.

Pheochromocytomas and paragangliomas are rare tumors of neural crest origin. Their remarkable genetic diversity and high heritability have enabled discoveries of bona fide cancer driver genes with an impact on diagnosis and clinical management and have consistently shed light on new paradigms in cancer. In this review, we explore unique mechanisms of pheochromocytoma and paraganglioma initiation and management by drawing from recent examples involving rare mutations of hypoxia-related genes VHL, EPAS1 and SDHB, and of a poorly known susceptibility gene, TMEM127. These models expand our ability to predict variant pathogenicity, inform new functional domains, recognize environmental-gene connections, and highlight persistent therapeutic challenges for tumors with aggressive behavior.

Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review.

INTRODUCTION: Approximately 7,000 newborns die every day, accounting for almost half of child deaths under 5 years of age. Deciphering which neonates are at increased risk for mortality can have an important global impact. As such, integrating high computational technology (e.g., artificial intelligence [AI]) may help identify the early and potentially modifiable predictors of neonatal mortality. Therefore, the objective of this study was to collate, critically appraise, and analyze neonatal prediction studies that included AI. METHODS: A literature search was performed in PubMed, Cochrane, OVID, and Google Scholar. We included studies that used AI (e.g., machine learning (ML) and deep learning) to formulate prediction models for neonatal death. We excluded small studies (n < 500 individuals) and studies using only antenatal factors to predict mortality. Two independent investigators screened all articles for inclusion. The data collection consisted of study design, number of models, features used per model, feature importance, internal and/or external validation, and calibration analysis. Our primary outcome was the average area under the receiving characteristic curve (AUC) or sensitivity and specificity for all models included in each study. RESULTS: Of 434 articles, 11 studies were included. The total number of participants was 1.26 M with gestational ages ranging from 22 weeks to term. Number of features ranged from 3 to 66 with timing of prediction as early as 5 min of life to a maximum of 7 days of age. The average number of models per study was 4, with neural network, random forest, and logistic regression comprising the most used models (58.3%). Five studies (45.5%) reported calibration plots and 2 (18.2%) conducted external validation. Eight studies reported results by AUC and 5 studies reported the sensitivity and specificity. The AUC varied from 58.3% to 97.0%. The mean sensitivities ranged from 63% to 80% and specificities from 78% to 99%. The best overall model was linear discriminant analysis, but it also had a high number of features (n = 17). DISCUSSION/CONCLUSION: ML models can accurately predict death in neonates. This analysis demonstrates the most commonly used predictors and metrics for AI prediction models for neonatal mortality. Future studies should focus on external validation, calibration, as well as deployment of applications that can be readily accessible to health-care providers.

Focal Adhesion Kinase in Ovarian Cancer: A Potential Therapeutic Target for Platinum and Taxane-Resistant Tumors.

Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase, which is an essential player in regulating cell migration, invasion, adhesion, proliferation, and survival. Its overexpression and activation have been identified in sixty-eight percent of epithelial ovarian cancer patients and this is significantly associated with higher tumor stage, metastasis, and shorter overall survival of these patients. Most recently, a new role has emerged for FAK in promoting resistance to taxane and platinum-based therapy in ovarian and other cancers. The development of resistance is a complex network of molecular processes that make the identification of a targetable biomarker in platinum and taxane-resistant ovarian cancer a major challenge. FAK overexpression upregulates ALDH and XIAP activity in platinum-resistant and increases CD44, YB1, and MDR-1 activity in taxaneresistant tumors. FAK is therefore now emerging as a prognostically significant candidate in this regard, with mounting evidence from recent successes in preclinical and clinical trials using small molecule FAK inhibitors. This review will summarize the significance and function of FAK in ovarian cancer, and its emerging role in chemotherapeutic resistance. We will discuss the current status of FAK inhibitors in ovarian cancers, their therapeutic competencies and limitations, and further propose that the combination of FAK inhibitors with platinum and taxane-based therapies could be an efficacious approach in chemotherapeutic resistant disease.