<b><i>Introduction:</i></b> 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. <b><i>Methods:</i></b> 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 (<i>n</i> &#x3c; 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. <b><i>Results:</i></b> 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 (<i>n</i> = 17). <b><i>Discussion/Conclusion:</i></b> 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 (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 makes 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 taxane-resistant 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.

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.

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.

Platinum resistance remains a major challenge in the chemotherapeutic management of ovarian cancer. The anti-diabetic drug metformin has been previously shown to induce cytotoxicity in platinum resistant ovarian cancer cells and overexpression and increased phosphorylation of a tyrosine kinase called Focal Adhesion Kinase (FAK) has been implicated in the development of this platinum resistance. Therefore, in the present study we evaluated the combined cytotoxic efficacy of Metformin and the focal adhesion kinase inhibitor 1,2,4,5-Benzenetetraamine tetrahydrochloride (Y15) in platinum resistant OVCAR3 ovarian cancer cells. Cells were initially treated with concentrations of Y15 ranging from 10-100 μM, and metformin from 10-100mM to determine 1C50 values. Subsequently, cells were treated with Y15 (80 μM) and metformin (26mM) alone and in combination. All treatments were triplicated with duration of 24hrs and control cells exposed to media only. The cytotoxic profile of each treatment was assessed using the automated trypan blue assay. DNA fragmentation and poly ADP ribose polymerase (PARP) cleavage assays were performed to evaluate the mechanism of cell death and we further evaluated the expression of phosphorylated FAK, p53 and p21 in response to treatments using western blot. Y15 alone produced 48% cell death. In combination, Y15 significantly increased the cytotoxic efficacy of metformin by 22%, when compared to the metformin only treatment. Cell death by apoptosis was confirmed by PARP cleavage and the presence of DNA fragments in Y15, metformin, and metformin +Y15 treatment groups. The Metformin +Y15 combination significantly downregulated the expression of phosphorylated FAK when compared to the individual treatments and control and this confirmed reduced FAK activity. Reduced FAK auto phosphorylation also correlated with increased expression of p53 AND p21 in metformin and Y15 treatment groups. Our findings show that Y15 significantly enhances the cytotoxic profile of metformin in platinum resistant OVCAR-3 cells. Furthermore, a FAK dependent apoptotic mechanism appears to underlie the cytotoxic effect of metformin as well as Y15 as both drugs significantly reduced the phosphorylation of FAK alone, and in combination. Reduced FAK activity also correlated with increased p53 and p21 expression. This study is the first to report a FAK dependent cytotoxic mechanism of metformin in ovarian cancer and in further work we will evaluate the mechanisms why which metformin cooperates with Y15 to inhibit FAK activity in platinum resistant ovarian cancer. Citation Format: Arkene S. Levy, Zara Khan, Samuel Batko, Keerthi Thallapureddy, Robert Smith, Thanigaivelan Kanagasabai, Julie Torruellas Garcia, Appu Rathinavelu. Evaluation of the cytotoxic profile of Metformin and Y15 in platinum resistant ovarian cancer cells. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2090.