Urban Environmental Threat Moderates the Relationship Between Depression and Insulin Resistance Among Latinxs With Type 2 Diabetes.

As the largest minoritised ethnic group in the United States, Latinxs face a greater risk for type 2 diabetes and depression. The aim of the present study was to explore whether the relationship between depressive symptoms and insulin resistance among Latinxs with type 2 diabetes was moderated by toxic stressors arising from urban environmental threat (i.e.

Depression and Anxiety Mediate the Relationship between Discrimination and Well-Being in a Sample of Latinx Adults with Type 2 Diabetes: Results from a Dual Mediation Analysis.

Latinxs experience greater risk for type 2 diabetes, discrimination, and poor mental health. The pathways linking these factors, however, are not well understood. This study tested whether depression and anxiety mediated the relationship between discrimination and well-being.

CD203c is expressed by human fetal hepatoblasts and distinguishes subsets of hepatoblastoma.

Background & Aims: Hepatocytic cells found during prenatal development have unique features compared to their adult counterparts, and are believed to be the precursors of pediatric hepatoblastoma. The cell-surface phenotype of hepatoblasts and hepatoblastoma cell lines was evaluated to discover new markers of these cells and gain insight into the development of hepatocytic cells and the phenotypes and origins of hepatoblastoma.

Methods: Human midgestation livers and four pediatric hepatoblastoma cell lines were screened using flow cytometry.

Bromodomain 4 inhibition leads to MYCN downregulation in Wilms tumor.

Background: Wilms tumor is the most common childhood kidney cancer. Two distinct histological subtypes of Wilms tumor have been described: tumors lacking anaplasia (the favorable subtype) and tumors displaying anaplastic features (the unfavorable subtype). Children with favorable disease generally have a very good prognosis, whereas those with anaplasia are oftentimes refractory to standard treatments and suffer poor outcomes, leading to an unmet clinical need.

Changes in quality of life following hypoglycaemia in adults with type 2 diabetes: A systematic review of longitudinal studies.

Aim: To conduct a systematic review of published studies reporting on the longitudinal impacts of hypoglycaemia on quality of life (QoL) in adults with type 2 diabetes.

Method: Database searches with no restrictions by language or date were conducted in MEDLINE, Cochrane Library, CINAHL and PsycINFO. Studies were included for review if they used a longitudinal design (e.

Defining an embryonal rhabdomyosarcoma endotype.

Rhabdomyosarcoma (RMS) is the most common childhood soft-tissue sarcoma. The largest subtype of RMS is embryonal rhabdomyosarcoma (ERMS) and accounts for 53% of all RMS. ERMS typically occurs in the head and neck region, bladder, or reproductive organs and portends a promising prognosis when localized; however, when metastatic the 5-yr overall survival rate is ∼43%.

Evaluating the consistency of large-scale pharmacogenomic studies.

Recent years have seen an increase in the availability of pharmacogenomic databases such as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) that provide genomic and functional characterization information for multiple cell lines. Studies have alluded to the fact that specific characterizations may be inconsistent between different databases. Analysis of the potential discrepancies in the different databases is highly significant, as these sources are frequently used to analyze and validate methodologies for personalized cancer therapies.

Volasertib preclinical activity in high-risk hepatoblastoma.

Relapsed and metastatic hepatoblastoma represents an unmet clinical need with limited chemotherapy treatment options. In a chemical screen, we identified volasertib as an agent with activity, inhibiting hepatoblastoma cell growth while sparing normal hepatocytes. Volasertib targets PLK1 and prevents the progression of mitosis, resulting in eventual cell death.

Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma.

Background: Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments.

Methods: Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination.

Sstack: an R package for stacking with applications to scenarios involving sequential addition of samples and features.

Summary: Biological processes are characterized by a variety of different genomic feature sets. However, often times when building models, portions of these features are missing for a subset of the dataset. We provide a modeling framework to effectively integrate this type of heterogeneous data to improve prediction accuracy.

Application of transfer learning for cancer drug sensitivity prediction.

Background: In precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale pharmacogenomics studies, like CCLE and GDSC hold the promise to mitigate the issue. However, one cannot directly employ data from multiple sources together due to the existing distribution shift in data.

Investigation of model stacking for drug sensitivity prediction.

Background: A significant problem in precision medicine is the prediction of drug sensitivity for individual cancer cell lines. Predictive models such as Random Forests have shown promising performance while predicting from individual genomic features such as gene expressions. However, accessibility of various other forms of data types including information on multiple tested drugs necessitates the examination of designing predictive models incorporating the various data types.

Heterogeneity Aware Random Forest for Drug Sensitivity Prediction.

Samples collected in pharmacogenomics databases typically belong to various cancer types. For designing a drug sensitivity predictive model from such a database, a natural question arises whether a model trained on diverse inter-tumor heterogeneous samples will perform similar to a predictive model that takes into consideration the heterogeneity of the samples in model training and prediction. We explore this hypothesis and observe that ensemble model predictions obtained when cancer type is known out-perform predictions when that information is withheld even when the samples sizes for the former is considerably lower than the combined sample size.

Combination therapy design for maximizing sensitivity and minimizing toxicity.

Background: Design of personalized targeted therapies involve modeling of patient sensitivity to various drugs and drug combinations. Majority of studies evaluate the sensitivity of tumor cells to targeted drugs without modeling the effect of the drugs on normal cells. In this article, we consider the individual modeling of drug responses to tumor and normal cells and utilize them to design targeted combination therapies that maximize sensitivity over tumor cells and minimize toxicity over normal cells.