Citations

Abstract The objective of this study is to describe application of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to support medical device real-world evaluation in a National Evaluation System for health Technology Coordinating Center (NESTcc) Test-Case involving 2 healthcare systems, Mercy Health and Mayo Clinic. CDM implementation was coordinated across 2 healthcare systems with multiple hospitals to aggregate both medical device data from supply chain databases and patient outcomes and covariates from electronic health record data. Several data quality assurance (QA) analyses were implemented on the OMOP CDM to validate the data extraction, transformation, and load (ETL) process. OMOP CDM-based data of relevant patient encounters were successfully established to support studies for FDA regulatory submissions. QA analyses verified that the data transformation was robust between data sources and OMOP CDM. Our efforts provided useful insights in real-world data integration using OMOP CDM for medical device evaluation coordinated across multiple healthcare systems.

Abstract Hearing loss is a risk factor for dementia; whether this association differs by race is unknown. Although hearing loss is less prevalent in Blacks than Whites, we hypothesized the hearing loss-dementia relationship is stronger in Blacks. All-cause dementia and mild cognitive impairment (MCI) were adjudicated using longitudinal cognitive data. Hearing was measured using pure tone better-ear thresholds (0.5-4 kHz). Cox proportional hazards models adjusted for demographic and clinical covariates and included an interaction term between hearing and race. In 3,605 participants from a population-based cohort (68-89 years, 23% Black, 59% female), estimates for 7-year dementia risk per 10 decibels increase in hearing loss were stronger in Blacks (hazard ratio [HR]:1.24, 95% Confidence Interval (CI):1.07,1.43) than Whites (HR:1.07; 95%CI:0.96,1.19). Hearing loss is a risk factor for dementia in Black Americans. These findings emphasize the need to address existing racial disparities in hearing healthcare.

Abstract Speech-in-noise performance is fundamental to daily communications and comprehensive characterization is needed. We studied 590 dementia-free participants aged 70-84 years, including 428 hearing-impaired participants from the Aging and Cognitive Health Evaluation in Elders (ACHIEVE) study baseline (2018-19), which is a randomized controlled trial partially nested within the Atherosclerosis Risk in Communities (ARIC) Study, and 162 normal-hearing ARIC Visit 6/7 (2016-17/2018-19) participants. The Quick Speech-in-Noise (QuickSIN) test was used to measure speech-in-noise performance. Predictors included (1) Four-frequency better-ear pure-tone average (PTA); (2) Magnetic resonance imaging (MRI) measures (total and lobar volumes, diffusion tensor imaging, white matter hyperintensities); (3) Global and domain-specific (language, memory, executive function) cognitive performance. All predictors were standardized to Z-scores. We used multivariable-adjusted linear regression, adjusting for demographic and disease covariates. PTA and cognitive performance, but not MRI measures, were independently associated with speech-in-noise performance, with PTA explaining the largest variance, indicating the promising role of hearing aids.

Abstract Bone marrow (BM) niche plays critical roles in promoting progression and chemoresistance of multiple myeloma (MM), but the iron metabolism bridging the malignant plasma cells and BM stromal cells (BMSCs) has not been well elucidated. Using in vitro and in vivo models of interaction of MM and BMSCs, we identified that iron level was augmented due to the elevated expression of transferrin in MM cells, and the accumulation of iron desensitized MM cells to proteasome inhibitors (PIs) treatment and promoted productions of cholesterol and lanosterol. Simultaneously, direct contact with BMSCs remarkably upregulated the protein level of GPX4, a ROS eraser, to obviate ferroptosis in MM cells. Mechanistic studies revealed that CD40/CD40 ligand (CD40L) signaling promoted the expressions of transferrin and SUMO-specific protease 3 (SENP3), and SENP3 stabilized GPX4 protein via deSUMOylation modification. GPX4 inhibitor induced ferroptosis was cell-cell contact dependent, since blocking CD40/CD40L interaction, or depletion of Cd40l in BMSCs from Cd40lfl/fl;Prx1Cre/+ mice, eliminated the anti-MM efficacy of RSL3 in the Vk*MYC mouse model of myeloma, but not in the patient-derived xenografts (PDX) model. Our study deciphers mechanism of iron metabolism in regulating MM chemoresistance, and clarifies the therapeutic potential of non-apoptosis strategies in managing refractory or relapsed MM patients.

AbstractBackgroundA recent phase III trial (NCT01372774) comparing use of stereotactic radiosurgery [SRS] versus whole-brain radiation therapy [WBRT] after surgical resection of a single brain metastasis revealed that declines in cognitive function were more common with WBRT than with SRS. A secondary endpoint in that trial, and the primary objective in this secondary analysis, was to identify baseline biomarkers associated with cognitive impairment after either form of radiotherapy for brain metastasis. Here we report our findings on APOE genotype and serum levels of associated proteins and their association with radiation-induced neurocognitive decline.MethodsIn this retrospective analysis of prospectively collected samples from a completed randomized clinical trial, patients provided blood samples every 3 months that were tested by genotyping and enzyme-linked immunosorbent assay, and results were analyzed in association with cognitive impairment.ResultsThe APOE genotype was not associated with neurocognitive impairment at 3 months. However, low serum levels of ApoJ, ApoE, or ApoA protein (all P < .01) and higher amyloid beta (Aβ 1–42) levels (P = .048) at baseline indicated a greater likelihood of neurocognitive decline at 3 months after SRS, whereas lower ApoJ levels were associated with decline after WBRT (P = .014).ConclusionsPatients with these pretreatment serum markers should be counseled about radiation-related neurocognitive decline.

Objectives: The diagnosis of lymphoma on cytologic preparations and core needle biopsies (CNBs) is often challenging due to limited material that precludes additional ancillary testing for accurate classification. We describe our institutional experience with patients who ultimately received a diagnosis of lymphoma using cytologic preparations and CNB material along with additional ancillary testing. Material and Methods: All patients whose cytology material (fluids, cytologic smears, and cell block) and CNB resulted in a diagnosis of lymphoma, atypical lymphoid population, or suspicious for lymphoma were retrieved. Diagnoses of lymphoma followed the established nomenclatures of the World Health Organization (WHO). For all cases, the ancillary test profiles were captured and summarized. Results: Of 389 cases identified, 17 (4.4%) were diagnosed as atypical lymphoid cells and 31 (7.9%) were suspicious for lymphoma resulting in an indeterminate rate of 12.3%. The rest of the malignant cases (341; 87.7%) were classified using 2017 WHO lymphoid classification system. Six low-grade and 26 high-grade B-cell lymphomas were not further classified because of limited tissue. B-cell lymphomas were the largest category (73.0%): Primarily diffuse large B-cell lymphoma (21.4%), followed by follicular lymphoma (17.5%) and chronic lymphocytic leukemia/small lymphocytic lymphoma (10.9%). A total of 140 cases (36.0%) had flow cytometry results, but 30 specimens (21.4%) were insufficient. Cytogenetic and molecular studies were performed for 78 cases (20.0%). Conclusion: Full classification of lymphomas is possible with combined cytologic preparations and CNB material and it is an attractive first sampling modality in the diagnostic process for this disease group.

Hexanucleotide G 4 C 2 repeat expansions in the C9orf72 gene are the most common genetic cause of amyotrophic lateral sclerosis and frontotemporal dementia. Dipeptide repeat proteins (DPRs) generated by translation of repeat-containing RNAs show toxic effects in vivo as well as in vitro and are key targets for therapeutic intervention. We generated human antibodies that bind DPRs with high affinity and specificity. Anti-GA antibodies engaged extra- and intra-cellular poly-GA and reduced aggregate formation in a poly-GA overexpressing human cell line. However, antibody treatment in human neuronal cultures synthesizing exogenous poly-GA resulted in the formation of large extracellular immune complexes and did not affect accumulation of intracellular poly-GA aggregates. Treatment with antibodies was also shown to directly alter the morphological and biochemical properties of poly-GA and to shift poly-GA/antibody complexes to more rapidly sedimenting ones. These alterations were not observed with poly-GP and have important implications for accurate measurement of poly-GA levels including the need to evaluate all centrifugation fractions and disrupt the interaction between treatment antibodies and poly-GA by denaturation. Targeting poly-GA and poly-GP in two mouse models expressing G 4 C 2 repeats by systemic antibody delivery for up to 16 mo was well-tolerated and led to measurable brain penetration of antibodies. Long-term treatment with anti-GA antibodies produced improvement in an open-field movement test in aged C9orf72 450 mice. However, chronic administration of anti-GA antibodies in AAV-(G 4 C 2 ) 149 mice was associated with increased levels of poly-GA detected by immunoassay and did not significantly reduce poly-GA aggregates or alleviate disease progression in this model.

Abstract The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.