A Synthetic Lethality Screen Using a Focused siRNA Library to Identify Sensitizers to Dasatinib Therapy for the Treatment of Epithelial Ovarian Cancer.

Molecular targeted therapies have been the focus of recent clinical trials for the treatment of patients with recurrent epithelial ovarian cancer (EOC). The majority have not fared well as monotherapies for improving survival of these patients. Poor bioavailability, lack of predictive biomarkers, and the presence of multiple survival pathways can all diminish the success of a targeted agent. Dasatinib is a tyrosine kinase inhibitor of the Src-family kinases (SFK) and in preclinical studies shown to have substantial activity in EOC. However, when evaluated in a phase 2 clinical trial for patients with recurrent or persistent EOC, it was found to have minimal activity. We hypothesized that synthetic lethality screens performed using a cogently designed siRNA library would identify second-site molecular targets that could synergize with SFK inhibition and improve dasatinib efficacy. Using a systematic approach, we performed primary siRNA screening using a library focused on 638 genes corresponding to a network centered on EGFR, HER2, and the SFK-scaffolding proteins BCAR1, NEDD9, and EFS to screen EOC cells in combination with dasatinib. We followed up with validation studies including deconvolution screening, quantitative PCR to confirm effective gene silencing, correlation of gene expression with dasatinib sensitivity, and assessment of the clinical relevance of hits using TCGA ovarian cancer data. A refined list of five candidates (CSNK2A1, DAG1, GRB2, PRKCE, and VAV1) was identified as showing the greatest potential for improving sensitivity to dasatinib in EOC. Of these, CSNK2A1, which codes for the catalytic alpha subunit of protein kinase CK2, was selected for additional evaluation. Synergistic activity of the clinically relevant inhibitor of CK2, CX-4945, with dasatinib in reducing cell proliferation and increasing apoptosis was observed across multiple EOC cell lines. This overall approach to improving drug efficacy can be applied to other targeted agents that have similarly shown poor clinical activity

Graves’ Disease Causing Pancytopenia and Autoimmune Hemolytic Anemia at Different Time Intervals: A Case Report and a Review of the Literature

Graves’ disease (GD) is associated with various hematologic abnormalities but pancytopenia and autoimmune hemolytic anemia (AIHA) are reported very rarely. Herein, we report a patient with GD who had both of these rare complications at different time intervals, along with a review of the related literature. The patient was a 70-year-old man who, during a hospitalization, was also noted to have pancytopenia and elevated thyroid hormone levels. Complete hematologic workup was unremarkable and his pancytopenia was attributed to hyperthyroidism. He was started on methimazole but unfortunately did not return for followup and stopped methimazole after a few weeks. A year later, he presented with fatigue and weight loss. Labs showed hyperthyroidism and isolated anemia (hemoglobin 7 g/dL). He had positive direct Coombs test and elevated reticulocyte index. He was diagnosed with AIHA and started on glucocorticoids. GD was confirmed with elevated levels of thyroid stimulating immunoglobulins and thyroid uptake and scan. He was treated with methimazole and radioactive iodine ablation. His hemoglobin improved to 10.7 g/dL at discharge without blood transfusion. Graves’ disease should be considered in the differential diagnosis of hematologic abnormalities. These abnormalities in the setting of GD generally respond well to antithyroid treatment

Lifestyle Modifications and Nutritional and Therapeutic Interventions in Delaying the Progression of Chronic Kidney Disease: A Review

Chronic kidney disease (CKD) is a debilitating progressive illness that affects more than 10% of the world\u27s population. In this literature review, we discussed the roles of nutritional interventions, lifestyle modifications, hypertension (HTN) and diabetes mellitus (DM) control, and medications in delaying the progression of CKD. Walking, weight loss, low-protein diet (LPD), adherence to the alternate Mediterranean (aMed) diet, and Alternative Healthy Eating Index (AHEI)-2010 slow the progression of CKD. However, smoking and binge alcohol drinking increase the risk of CKD progression. In addition, hyperglycemia, altered lipid metabolism, low-grade inflammation, over-activation of the renin-angiotensin-aldosterone system (RAAS), and overhydration (OH) increase diabetic CKD progression. The Kidney Disease: Improving Global Outcomes (KDIGO) guidelines recommend blood pressure (BP) control of \u3c140/90 mmHg in patients without albuminuria and \u3c130/80 mmHg in patients with albuminuria to prevent CKD progression. Medical therapies aim to target epigenetic alterations, fibrosis, and inflammation. Currently, RAAS blockade, sodium-glucose cotransporter-2 (SGLT2) inhibitors, pentoxifylline, and finerenone are approved for managing CKD. In addition, according to the completed Study of Diabetic Nephropathy with Atrasentan (SONAR), atrasentan, an endothelin receptor antagonist (ERA), decreased the risk of renal events in diabetic CKD patients. However, ongoing trials are studying the role of other agents in slowing the progression of CKD

Characterization of adolescent and pediatric renal cell carcinoma: A report from the Children's Oncology Group study AREN03B2

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111918/1/cncr29368.pd

Intelligent Energy Management across Smart Grids Deploying 6G IoT, AI, and Blockchain in Sustainable Smart Cities

© 2024 The Author(s). Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.Peer reviewe

Network analysis of large-scale ImmGen and Tabula Muris datasets highlights metabolic diversity of tissue mononuclear phagocytes

The diversity of mononuclear phagocyte (MNP) subpopulations across tissues is one of the key physiological characteristics of the immune system. Here, we focus on understanding the metabolic variability of MNPs through metabolic network analysis applied to three large-scale transcriptional datasets: we introduce (1) an ImmGen MNP open-source dataset of 337 samples across 26 tissues; (2) a myeloid subset of ImmGen Phase I dataset (202 MNP samples); and (3) a myeloid mouse single-cell RNA sequencing (scRNA-seq) dataset (51,364 cells) assembled based on Tabula Muris Senis. To analyze such large-scale datasets, we develop a network-based computational approach, genes and metabolites (GAM) clustering, for unbiased identification of the key metabolic subnetworks based on transcriptional profiles. We define 9 metabolic subnetworks that encapsulate the metabolic differences within MNP from 38 different tissues. Obtained modules reveal that cholesterol synthesis appears particularly active within the migratory dendritic cells, while glutathione synthesis is essential for cysteinyl leukotriene production by peritoneal and lung macrophages

A phase 1/2 open label nonrandomized clinical trial of intravenous 2-hydroxypropyl-β-cyclodextrin for acute liver disease in infants with Niemann-Pick C1

Introduction: Niemann-Pick C (NPC) is an autosomal recessive disease due to defective NPC1 or NPC2 proteins resulting in Methods: Infants received intravenous 2HPBCD twice a week for 6 weeks, followed by monthly infusion for 6-months. Primary outcome measure was reduction of plasma (3β,5α,6β-trihydroxy-cholan-24-oyl) glycine (TCG), a bile acid generated from cholesterol sequestered in lysosome. Results: Three participants completed this protocol. A fourth patient received intravenous 2HPBCD under an emergency investigational new drug study but later expired from her underlying condition. The three protocol patients are living and have improved liver enzymes and TCG. No patient has experienced a drug-related adverse event. Conclusion: Intravenous 2HPBCD was tolerated in three infants with liver disease due to NPC