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Abstract Atrial myxomas are typically found in the left atrium and are the most common among overall rare cardiac tumors. Herein, we describe the clinical course of a 72-year-old female with non-small cell lung adenocarcinoma found to have an atrial mass during an imaging for evaluation for lung cancer progression. Differentiating between distinct types of masses can pose a challenge to the treatment team especially in the setting of exiting malignancy. This case demonstrates the complex decision making involved in the diagnosis, and timing of intervention to remove atrial mass in patients with frailty and a fast-growing cardiac mass.

IntroductionIn collaboration with the Minnesota Hmong community, we have previously discovered significant differences in allele frequencies for key Single Nucleotide Variations (SNVs) within Very Important Pharmacogenes (VIPs) between Hmong and East Asians. Recognizing the potential clinical implications of these observed differences, we sought to validate these observations in a Hmong cohort residing in California, the state with the largest Hmong population in the US. Robust validation of these differences would affect motivation for clinicians treating individuals who identify as Hmong to consider pharmacogenomic (PGx) testing as a means to improve clinical decision making when using therapeutic agents in this unique population.MethodGuided by California Hmong community leaders and utilizing the basic approach of community-based participatory research, demographic, clinical information and a buccal swab was obtained from Hmong adults residing in California. A commercial PGx testing panel was performed on these samples and specific allele frequencies of interest were compared between California and Minnesota Hmong. Allele frequency differences between California Hmong, East Asians and Europeans, were also compared. Return-of-PGx-results and presentations of group data were made to members of the Hmong along with PGx educational sessions to help interpret the observations.ResultsIn 118 California Hmong who completed the study, the allele frequencies for SNV’s were similar to previous Minnesota Hmong results. Furthermore, out of the 18 SNVs that were not previously reported in Hmong, allele frequencies were statistically different in 38% (7/18) of SNVs comparing California Hmong to East Asians, and in 77.8% (14/18) SNVs comparing California Hmong to Europeans.ConclusionThese results validate the original study’s findings that Hmong people living in different US locations have similar allele frequencies for key PGx genes. Further, for many of these PGx genes, their allele frequencies are significantly different compared to either East Asians or Europeans. Clinicians should consider these important differences when prescribing medications for people who identify as Hmong.

As cancer therapies increase in effectiveness and patients' life expectancies improve, balancing oncologic efficacy while reducing acute and long-term cardiovascular toxicities has become of paramount importance. To address this pressing need, the Cardiology Oncology Innovation Network (COIN) was formed to bring together domain experts with the overarching goal of collaboratively investigating, applying, and educating widely on various forms of innovation to improve the quality of life and cardiovascular healthcare of patients undergoing and surviving cancer therapies. The COIN mission pillars of innovation, collaboration, and education have been implemented with cross-collaboration among academic institutions, private and public establishments, and industry and technology companies. In this report, we summarize proceedings from the first two annual COIN summits (inaugural in 2020 and subsequent in 2021) including educational sessions on technological innovations for establishing best practices and aligning resources. Herein, we highlight emerging areas for innovation and defining unmet needs to further improve the outcome for cancer patients and survivors of all ages. Additionally, we provide actionable suggestions for advancing innovation, collaboration, and education in cardio-oncology in the digital era.

ObjectiveThe goal of this work is to reduce the amount of manual work required to go from data capture to regulatory submission. It will be shown that the use of Siamese networks will allow for the generation of embeddings that can be used by traditional machine learning classifiers to perform the classification at much higher levels of accuracy than standard approaches.MethodsSiamese networks are a method for training data embeddings such that data within the same class are closer with respect to a given distance metric than they are to data points in another class. Because they are designed to learn similarity within pairs of data points, they work well in situations where the number of classes is relatively large compared to the number of training samples. In this work, we will show that embeddings generated via a Siamese network from metadata associated with electronic data capture forms can be used to predict the associated SDTM field.ResultsWith a relatively simple network coupled with a basic classification algorithm, the proposed method can achieve accuracies greater than 90%, which is significantly higher than what has been achieved with traditional methods, with many of the inaccurate mappings due to a lack of training data. In many cases, there is a 15% increase in accuracy vs. more traditional methods.ConclusionLeveraging Siamese networks, it is possible to generate embeddings that efficiently represent data fields in a lower dimensional space. This allows the creation of a system that can automatically map between data schemas at high levels of accuracy. Such systems represent the first step in automating one of the many labor-intensive data management tasks associated with clinical trials.

Cerebral sparganosis is a rare parasitic infection that can be difficult to diagnose due to similarities in clinical and imaging features with more common parasitic infections, such as neurocysticercosis, and other neurologic conditions. We present a case of a 61-year-old female with a verified case of cerebral sparganosis. We also review the current literature regarding the sparganosis infection process, imaging features, and unique therapeutic options. With awareness of the risk factors for cerebral sparganosis and consideration for this diagnosis when the response to therapy for other organisms appears suboptimal, the time to accurate diagnosis may be reduced, and treatment outcomes improved.

Abstract Mild Traumatic Brain Injury (MTBI) patients with persistent headaches are known to have diminished supraspinal modulatory connectivity from their prefrontal cortices. Repetitive transcranial magnetic stimulation (rTMS) is able to alleviate MTBI-related headache (MTBI-HA). This functional magnetic resonance imaging (fMRI) study assessed supraspinal correlates associated with the headache analgesic effect of rTMS at left prefrontal cortex (LPFC), hypothesizing real rTMS would significantly increase modulatory functions at LPFC in comparison to sham treatment. Subjects with MTBI-HA were randomized to receive either real or sham rTMS treatments and subjected to pre- and post-treatment resting state and evoked heat-pain fMRI as described in a prior study. Real rTMS consisted of 2000 pulses delivered at 10 Hz and 80% of the resting motor threshold at left dorsolateral prefrontal cortex, whereas sham treatment was delivered with same figure-of-eight coil turned 180 degrees. Follow-up fMRI was performed one-week post-treatment. All fMRI data was processed using BrainVoyager QX Software. 14 subjects receiving real and 12 subjects receiving sham treatments completed the study. The REAL group demonstrated significant (P

Exposure-response (E-R) is a key aspect of pharmacometrics analysis that supports drug dose selection. Currently, there is a lack of understanding of the technical considerations necessary for drawing unbiased estimates from data. Due to recent advances in machine learning (ML) explainability methods, ML has garnered significant interest for causal inference. To this end, we used simulated datasets with known E-R “ground truth” to generate a set of good practices for the development of ML models required to avoid introducing biases when performing causal inference. These practices include the use of causal diagrams to enable the careful consideration of model variables by which to obtain desired E-R relationship insights, keeping a strict separation of data for model-training and for inference generation to avoid biases, hyperparameter tuning to improve the reliability of models, and estimating proper confidence intervals around inferences using a bootstrap sampling with replacement strategy. We computationally confirm the benefits of the proposed ML workflow by using a simulated dataset with nonlinear and non-monotonic exposure–response relationships.

Artificial intelligence (AI) and machine learning (ML) have transformed the landscape of data management in higher education institutions, necessitating a critical evaluation of existing data privacy policies and practices. This research delves into the inadequacies of current frameworks in adapting to the swift evolution of Big Data. Student, faculty, and staff perspectives on data privacy are examined in terms of how their viewpoints influence university policies. Data privacy incidents at several universities are discussed to identify patterns and extract lessons learned. Recommendations and best practices for enhancing data privacy in the context of AI/ML implementation are discussed, with an emphasis on the need for policy reform and improved protocols to safeguard student privacy effectively. The integration of AI/ML in higher education must align with robust data privacy standards, enabling institutions to optimize teaching, learning, and administrative processes while safeguarding sensitive student information.

Knottin is a cystine knot peptide. Here, the authors develop a knottin-based tracer for positron emission tomography and demonstrate its ability to detect cancer and idiopathic pulmonary fibrosis through selective binding to integrin αvβ6.

ObjectiveOne of the greatest challenges in clinical trial design is dealing with the subjectivity and variability introduced by human raters when measuring clinical end-points. We hypothesized that robotic measures that capture the kinematics of human movements collected longitudinally in patients after stroke would bear a significant relationship to the ordinal clinical scales and potentially lead to the development of more sensitive motor biomarkers that could improve the efficiency and cost of clinical trials.Materials and methodsWe used clinical scales and a robotic assay to measure arm movement in 208 patients 7, 14, 21, 30 and 90 days after acute ischemic stroke at two separate clinical sites. The robots are low impedance and low friction interactive devices that precisely measure speed, position and force, so that even a hemiparetic patient can generate a complete measurement profile. These profiles were used to develop predictive models of the clinical assessments employing a combination of artificial ant colonies and neural network ensembles.ResultsThe resulting models replicated commonly used clinical scales to a cross-validated R2 of 0.73, 0.75, 0.63 and 0.60 for the Fugl-Meyer, Motor Power, NIH stroke and modified Rankin scales, respectively. Moreover, when suitably scaled and combined, the robotic measures demonstrated a significant increase in effect size from day 7 to 90 over historical data (1.47 versus 0.67).Discussion and conclusionThese results suggest that it is possible to derive surrogate biomarkers that can significantly reduce the sample size required to power future stroke clinical trials.

Background: One of the keys to running a successful clinical trial is the selection of high quality clinical sites, i.e., sites that are able to enroll patients quickly, engage them on an ongoing basis to prevent drop-out, and execute the trial in strict accordance to the clinical protocol. Intuitively, the historical track record of a site is one of the strongest predictors of its future performance; however, issues such as data availability and wide differences in protocol complexity can complicate interpretation. Here, we demonstrate how operational data derived from central laboratory services can provide key insights into the performance of clinical sites and help guide operational planning and site selection for new clinical trials. Methods: Our methodology uses the metadata associated with laboratory kit shipments to clinical sites (such as trial and anonymized patient identifiers, investigator names and addresses, sample collection and shipment dates, etc.) to reconstruct the complete schedule of patient visits and derive insights about the operational performance of those sites, including screening, enrollment, and drop-out rates and other quality indicators. This information can be displayed in its raw form or normalized to enable direct comparison of site performance across studies of varied design and complexity. Results: Leveraging Covance's market leadership in central laboratory services, we have assembled a database of operational metrics that spans more than 14,000 protocols, 1400 indications, 230,000 unique investigators, and 23 million patient visits and represents a significant fraction of all clinical trials run globally in the last few years. By analyzing this historical data, we are able to assess and compare the performance of clinical investigators across a wide range of therapeutic areas and study designs. This information can be aggregated across trials and geographies to gain further insights into country and regional trends, sometimes with surprising results. Conclusions: The use of operational data from Covance Central Laboratories provides a unique perspective into the performance of clinical sites with respect to many important metrics such as patient enrollment and retention. These metrics can, in turn, be used to guide operational planning and site selection for new clinical trials, thereby accelerating recruitment, improving quality, and reducing cost.

Triply Periodic Minimal Surface (TPMS) structures fabricated via Additive Manufacturing (AM) have recently emerged as being appropriate candidates for high-value engineered structures, including porous bio-implants and energy absorbing structures. Among the many TPMS designs, Gyroid structures have demonstrated merits in AM manufacturability, mechanical properties, and permeability in comparison to traditional lattice structures. Gyroid structures are mathematically formulated by geometric factors: surface thickness, sample size, number of surface periods, and the associated isovalue. These factors result in a continuous surface with a topology-specific structural response. Quantifying the effect of these factors on overall structural response requires substantial computational and experimental resources, and little systematic data exists in the literature. Using a numerical approach, cubic Gyroid structures of various designs were simulated under quasi-static compression, using a simulation model verified with experimental data for AM Ti-6Al-4V specimens fabricated by Selective Laser Melting (SLM). The influence of geometric factors on structural response was quantified with OFAT (One Factor At a Time) and Taguchi methods. The results identify the number of cells and surface thickness strongly influence both modulus and compressive strength. These findings were used to theoretically develop a Gyroid structure that imitates both elastic modulus and compressive strength of human cortical bone. Keywords: Finite element analysis, Triply periodic minimal surface, Cellular structure, Taguchi, Additive manufacture, 3D printing

STK17A is a relatively uncharacterized member of the death-associated protein family of serine/threonine kinases which have previously been associated with cell death and apoptosis. Our prior work established that STK17A is a novel p53 target gene that is induced by a variety of DNA damaging agents in a p53-dependent manner. In this study we have uncovered an additional, unanticipated role for STK17A as a candidate promoter of cell proliferation and survival in glioblastoma (GBM). Unexpectedly, it was found that STK17A is highly overexpressed in a grade-dependent manner in gliomas compared to normal brain and other cancer cell types with the highest level of expression in GBM. Knockdown of STK17A in GBM cells results in a dramatic alteration in cell shape that is associated with decreased proliferation, clonogenicity, migration, invasion and anchorage independent colony formation. STK17A knockdown also sensitizes GBM cells to genotoxic stress. STK17A overexpression is associated with a significant survival disadvantage among patients with glioma which is independent of age, molecular phenotype, IDH1 mutation, PTEN loss, and alterations in the p53 pathway and partially independent of grade. In summary, we demonstrate that STK17A provides a proliferative and survival advantage to GBM cells and is a potential target to be exploited therapeutically in patients with glioma.

Both bone marrow (BM) and myocardium contain progenitor cells expressing the c-Kit tyrosine kinase. The aims of this study were to determine the effects of c-Kit mutations on: i. myocardial c-Kit(+) cells counts and ii. the stability of left ventricular (LV) contractile function and structure during aging. LV structure and contractile function were evaluated (echocardiography) in two groups of Kit mutant (W/Wv and W41/W42) and in wild type (WT) mice at 4 and 12 months of age and the effects of the mutations on LV mass, vascular density and the numbers of proliferating cells were also determined. In 4 month old Kit mutant and WT mice, LV ejection fractions (EF) and LV fractional shortening rates (FS) were comparable. At 12 months of age EF and FS were significantly decreased and LV mass was significantly increased only in W41/W42 mice. Myocardial vascular densities and c-Kit(+) cell numbers were significantly reduced in both mutant groups when compared to WT hearts. Replacement of mutant BM with WT BM at 4 months of age did not prevent these abnormalities in either mutant group although they were somewhat attenuated in the W/Wv group. Notably BM transplantation did not prevent the development of cardiomyopathy in 12 month W41/W42 mice. The data suggest that decreased numbers and functional capacities of c-Kit(+) cardiac resident progenitor cells may be the basis of the cardiomyopathy in W41/W42 mice and although defects in mutant BM progenitor cells may prove to be contributory, they are not causal.

Disruption of lipid and carbohydrate homeostasis is an important factor in the development of prevalent metabolic diseases such as diabetes, obesity, and atherosclerosis. Therefore, small molecules that could reduce insulin dependence and regulate dyslipidemia could have a dramatic effect on public health. The grapefruit flavonoid naringenin has been shown to normalize lipids in diabetes and hypercholesterolemia, as well as inhibit the production of HCV. Here, we demonstrate that naringenin regulates the activity of nuclear receptors PPARalpha, PPARgamma, and LXRalpha. We show it activates the ligand-binding domain of both PPARalpha and PPARgamma, while inhibiting LXRalpha in GAL4-fusion reporters. Using TR-FRET, we show that naringenin is a partial agonist of LXRalpha, inhibiting its association with Trap220 co-activator in the presence of TO901317. In addition, naringenin induces the expression of PPARalpha co-activator, PGC1alpha. The flavonoid activates PPAR response element (PPRE) while suppressing LXRalpha response element (LXRE) in human hepatocytes, translating into the induction of PPAR-regulated fatty acid oxidation genes such as CYP4A11, ACOX, UCP1 and ApoAI, and inhibition of LXRalpha-regulated lipogenesis genes, such as FAS, ABCA1, ABCG1, and HMGR. This effect results in the induction of a fasted-like state in primary rat hepatocytes in which fatty acid oxidation increases, while cholesterol and bile acid production decreases. Our findings explain the myriad effects of naringenin and support its continued clinical development. Of note, this is the first description of a non-toxic, naturally occurring LXRalpha inhibitor.