Biology subjects

Hadley, D.

Publications and source records attributed to Hadley, D..

3 recordsLinked to original sources

bioRxiv2025

Validation of Extracellular Vesicles in FACTORFIVEs EXOFECTA LyoBeads

ObjectiveThe objective of this project was to determine if extracellular vesicles (EVs) were present in FACTORFIVEs EXOFECTA LyoBeads to validate flash freezing and subsequent lyophilization as an effective EV preservation method. SummaryEVs were isolated from resuspended EXOFECTA LyoBeads using differential ultracentrifugation and subjected to further characterization techniques such as nanoparticle tracking analysis (NTA), single particle flow cytometry, and cryo-electron microscopy (cryo-EM). First, NTA revealed particles with size distributions and concentrations consistent with the expected characteristics of EVs, indicating successful isolation from samples. Secondly, flow cytometry was performed using antibodies targeting the canonical EV tetraspanins CD9, CD63, and CD81. Detection of these markers supported the presence of enriched EVs in the samples. Finally, cryo-EM was performed to confirm the existence of vesicular bodies and their morphology. Visualization of the samples revealed membrane-enclosed particles with a visible lipid bilayer, consistent with EVs. Overall, the results support EVs were in EXOFECTA LyoBeads, indicating EV retention in the final product after FACTORFIVEs method of flash freezing, stabilizing, and lyophilization.

molecular biology↗

bioRxiv2018

Tracing diagnosis trajectories over millions of inpatients reveal an unexpected association between schizophrenia and rhabdomyolysis

While it has been technically feasible to create longitudinal representations of individual health at a nationwide scale, the use of these techniques to identify novel disease associations for the risk stratification of patients has had limited success. Here, we created a large-scale US longitudinal disease network of traced readmission patterns (i.e., disease trajectories), merging data from over 10.4 million inpatients from 350 California hospitals through the Healthcare Cost and Utilization Project between 1980 and 2010. We were able to create longitudinal representations of disease progression mapping over 300 common diseases, including the well-known complication of heart failure after acute myocardial infarction. Surprisingly, out of these generated disease trajectories, we discovered an unknown association between schizophrenia, a chronic mental disorder, and rhabdomyolysis, a rare disease of muscle breakdown. It was found that 92 of 3674 patients (2.5%) with schizophrenia were readmitted for rhabdomyolysis (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95] P-value 9.54E-15), which has a general population incidence of 1 in 10,000. We validated this association using independent electronic health records from over 830,000 patients treated over seven years at the University of California, San Francisco (UCSF) medical center. A case review of 29 patients at UCSF who were treated for schizophrenia and who went on to develop rhabdomyolysis demonstrated that the majority of cases (62%) are idiopathic, which suggests a biological connection between these two diseases. Together, these findings demonstrate the power of using public disease registries in combination with electronic medical records to discover novel disease associations.\n\nOne Sentence SummaryBased on the longitudinal health records from millions of California inpatient discharges, we created a temporal network that enabled us to understand statewide patterns of hospital readmissions, which led to the novel finding that hospitalization for schizophrenia is significantly associated with rehospitalization for rhabdomyolysis.

bioinformatics↗

bioRxiv2016

Systematic integration of biomedical knowledge prioritizes drugs for repurposing

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data was integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then we predicted the probability of treatment for 209,168 compound-disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members.

bioinformatics↗