Theoretical Investigation of Carbon Dioxide Adsorption on Li+-Decorated Nanoflakes.

Recently, the capture of carbon dioxide, the primary greenhouse gas, has attracted particular interest from researchers worldwide. In the present work, several theoretical methods have been used to study adsorption of CO2 molecules on Li+-decorated coronene (Li+@coronene). It has been established that Li+ can be strongly anchored on coronene, and then a physical adsorption of CO2 will occur in the vicinity of this cation. Moreover, such a decoration has substantially improved interaction energy (Eint) between CO2 molecules and the adsorbent. One to twelve CO2 molecules per one Li+ have been considered, and their Eint values are in the range from -5.55 to -16.87 kcal/mol. Symmetry-adapted perturbation theory (SAPT0) calculations have shown that, depending on the quantity of adsorbed CO2 molecules, different energy components act as the main reason for attraction. AIMD simulations allow estimating gravimetric densities (GD, wt.%) at various temperatures, and the maximal GDs have been calculated to be 9.3, 6.0, and 4.9% at T = 77, 300, and 400 K, respectively. Besides this, AIMD calculations validate stability of Li+@coronene complexes during simulation time at the maximum CO2 loading. Bader's atoms-in-molecules (QTAIM) and independent gradient model (IGM) techniques have been implemented to unveil the features of interactions between CO2 and Li+@coronene. These methods have proved that there exists a non-covalent bonding between the cation center and CO2. We suppose that findings, derived in this theoretical work, may also benefit the design of novel nanosystems for gas storage and delivery.

Strong Components of Epigenetic Memory in Cultured Human Fibroblasts Related to Site of Origin and Donor Age.

Differentiating pluripotent cells from fibroblast progenitors is a potentially transformative tool in personalized medicine. We previously identified relatively greater success culturing dura-derived fibroblasts than scalp-derived fibroblasts from postmortem tissue. We hypothesized that these differences in culture success were related to epigenetic differences between the cultured fibroblasts by sampling location, and therefore generated genome-wide DNA methylation and transcriptome data on 11 intrinsically matched pairs of dural and scalp fibroblasts from donors across the lifespan (infant to 85 years). While these cultured fibroblasts were several generations removed from the primary tissue and morphologically indistinguishable, we found widespread epigenetic differences by sampling location at the single CpG (N = 101,989), region (N = 697), "block" (N = 243), and global spatial scales suggesting a strong epigenetic memory of original fibroblast location. Furthermore, many of these epigenetic differences manifested in the transcriptome, particularly at the region-level. We further identified 7,265 CpGs and 11 regions showing significant epigenetic memory related to the age of the donor, as well as an overall increased epigenetic variability, preferentially in scalp-derived fibroblasts-83% of loci were more variable in scalp, hypothesized to result from cumulative exposure to environmental stimuli in the primary tissue. By integrating publicly available DNA methylation datasets on individual cell populations in blood and brain, we identified significantly increased inter-individual variability in our scalp- and other skin-derived fibroblasts on a similar scale as epigenetic differences between different lineages of blood cells. Lastly, these epigenetic differences did not appear to be driven by somatic mutation--while we identified 64 probable de-novo variants across the 11 subjects, there was no association between mutation burden and age of the donor (p = 0.71). These results depict a strong component of epigenetic memory in cell culture from primary tissue, even after several generations of daughter cells, related to cell state and donor age.

Developing a predictive model for metastatic potential in pancreatic neuroendocrine tumor.

CONTEXT: Pancreatic neuroendocrine tumors (PNETs) exhibit a wide range of behavior from localized disease to aggressive metastasis. A comprehensive transcriptomic profile capable of differentiating between these phenotypes remains elusive. OBJECTIVE: Use machine learning to develop predictive models of PNET metastatic potential dependent upon transcriptomic signature. METHODS: RNA-sequencing data were analyzed from 95 surgically-resected primary PNETs in an international cohort. Two cohorts were generated with equally balanced metastatic PNET composition. Machine learning was used to create predictive models distinguishing between localized and metastatic tumors. Models were validated on an independent cohort of 29 formalin-fixed, paraffin-embedded samples using NanoString nCounter®, a clinically-available mRNA quantification platform. RESULTS: Gene expression analysis identified concordant differentially expressed genes between the two cohorts. Gene set enrichment analysis identified additional genes that contributed to enriched biologic pathways in metastatic PNETs. Expression values for these genes were combined with an additional 7 genes known to contribute to PNET oncogenesis and prognosis, including ARX and PDX1. Eight specific genes (AURKA, CDCA8, CPB2, MYT1L, NDC80, PAPPA2, SFMBT1, ZPLD1) were identified as sufficient to classify the metastatic status with high sensitivity (87.5% - 93.8%) and specificity (78.1% - 96.9%). These models remained predictive of the metastatic phenotype using NanoString nCounter® on the independent validation cohort, achieving a median AUROC of 0.886. CONCLUSIONS: We identified and validated an eight-gene panel predictive of the metastatic phenotype in PNETs, which can be detected using the clinically-available NanoString nCounter® system. This panel should be studied prospectively to determine its utility in guiding operative versus non-operative management.

Sex-Based Clinicopathologic and Survival Differences Among Patients with Pancreatic Neuroendocrine Tumors.

INTRODUCTION: Sex-based differences in survival have emerged among patients with pancreatic neuroendocrine tumors (PNETs). Mechanisms driving these differences remain poorly understood. We aimed to further characterize sex-based clinicopathologic and survival differences among patients with PNETs and correlate divergent mutational signatures in these patients. METHODS: The National Cancer Database (NCDB) was queried for PNET patients diagnosed 2004-2017 who underwent surgery. Clinicopathologic features were analyzed by sex. The overall survival (OS) of men and women by disease stage was compared using the Kaplan-Meier method. Differences in PNET mutational signatures were analyzed by querying the American Association for Cancer Research Genomics Evidence Neoplasia Information (AACR-GENIE) Cohort v11.0-public. Frequencies of mutational signatures were compared by Fischer's exact (FE) test, adjusting for multiple testing via the Benjamini-Hochberg correction. RESULTS: About 15,202 patients met inclusion criteria from the NCDB; 51.9% were men and 48.1% were women. Men more frequently had tumors > 2 cm than women and more commonly had poorly or undifferentiated tumors. Despite this, lymph node positivity and distant metastases were similar. Differences in OS were only seen among those with early stage rather than stage 3 or 4 disease. MEN1 and DAXX mutations were more frequent among men with PNETs, whereas TP53 mutations were more frequent among women when assessed by FE test. However, neither of these mutational differences maintained statistical significance when adjusted for multiple testing. CONCLUSION: Compared to women, men have larger tumors but similar rates of distant metastases at time of surgery. OS differences appear to be driven by patients with early-stage disease without clearly identifiable differences in mutational signatures between the sexes.

Association of the Affordable Care Act with access to highest-volume centers for patients with thyroid cancer.

BACKGROUND: Disparities exist in access to high-volume surgeons, who have better outcomes after thyroidectomy. The association of the Affordable Care Act's Medicaid expansion with access to high-volume thyroid cancer surgery centers remains unclear. METHODS: The National Cancer Database was queried for all adult thyroid cancer patients diagnosed from 2010 to 2016. Hospital quartiles (Q1-4) defined by operative volume were generated. Clinicodemographics and adjusted odds ratios for treatment per quartile were analyzed by insurance status. An adjusted difference-in-differences analysis examined the association between implementation of the Affordable Care Act and changes in payer mix by hospital quartile. RESULTS: In total, 241,448 patients were included. Medicaid patients were most commonly treated at Q3-Q4 hospitals (Q3 odds ratios 1.05, P = .020, Q4 1.11, P < .001), whereas uninsured patients were most often treated at Q2-Q4 hospitals (Q2 odds ratios 2.82, Q3 2.34, Q4 2.07, P < .001). After expansion, Medicaid patients had lower odds of surgery at Q3-Q4 compared with Q1 hospitals (odds ratios Q3 0.82, P < .001 Q4 0.85, P = .002) in expansion states, but higher odds of treatment at Q3-Q4 hospitals in nonexpansion states (odds ratios Q3 2.23, Q4 1.86, P < .001). Affordable Care Act implementation was associated with increased proportions of Medicaid patients within each quartile in expansion compared with nonexpansion states (Q1 adjusted difference-in-differences 5.36%, Q2 5.29%, Q3 3.68%, Q4 3.26%, P < .001), and a decrease in uninsured patients treated at Q4 hospitals (adjusted difference-in-differences -1.06%, P = .001). CONCLUSIONS: Medicaid expansion was associated with an increased proportion of Medicaid patients undergoing thyroidectomy for thyroid cancer in all quartiles, with increased Medicaid access to high-volume centers in expansion compared with nonexpansion states.

Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions.

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling. We applied these methods to clinical specimens gathered from 669 patients in New York City during the first two months of the outbreak, yielding a broad molecular portrait of the emerging COVID-19 disease. We find significant enrichment of a NYC-distinctive clade of the virus (20C), as well as host responses in interferon, ACE, hematological, and olfaction pathways. In addition, we use 50,821 patient records to find that renin-angiotensin-aldosterone system inhibitors have a protective effect for severe COVID-19 outcomes, unlike similar drugs. Finally, spatial transcriptomic data from COVID-19 patient autopsy tissues reveal distinct ACE2 expression loci, with macrophage and neutrophil infiltration in the lungs. These findings can inform public health and may help develop and drive SARS-CoV-2 diagnostic, prevention, and treatment strategies.

Shotgun Transcriptome and Isothermal Profiling of SARS-CoV-2 Infection Reveals Unique Host Responses, Viral Diversification, and Drug Interactions.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused thousands of deaths worldwide, including >18,000 in New York City (NYC) alone. The sudden emergence of this pandemic has highlighted a pressing clinical need for rapid, scalable diagnostics that can detect infection, interrogate strain evolution, and identify novel patient biomarkers. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs, plus a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, bacterial, and viral profiling. We applied both technologies across 857 SARS-CoV-2 clinical specimens and 86 NYC subway samples, providing a broad molecular portrait of the COVID-19 NYC outbreak. Our results define new features of SARS-CoV-2 evolution, nominate a novel, NYC-enriched viral subclade, reveal specific host responses in interferon, ACE, hematological, and olfaction pathways, and examine risks associated with use of ACE inhibitors and angiotensin receptor blockers. Together, these findings have immediate applications to SARS-CoV-2 diagnostics, public health, and new therapeutic targets.

Divergent neuronal DNA methylation patterns across human cortical development reveal critical periods and a unique role of CpH methylation.

BACKGROUND: DNA methylation (DNAm) is a critical regulator of both development and cellular identity and shows unique patterns in neurons. To better characterize maturational changes in DNAm patterns in these cells, we profile the DNAm landscape at single-base resolution across the first two decades of human neocortical development in NeuN+ neurons using whole-genome bisulfite sequencing and compare them to non-neurons (primarily glia) and prenatal homogenate cortex. RESULTS: We show that DNAm changes more dramatically during the first 5 years of postnatal life than during the entire remaining period. We further refine global patterns of increasingly divergent neuronal CpG and CpH methylation (mCpG and mCpH) into six developmental trajectories and find that in contrast to genome-wide patterns, neighboring mCpG and mCpH levels within these regions are highly correlated. We integrate paired RNA-seq data and identify putative regulation of hundreds of transcripts and their splicing events exclusively by mCpH levels, independently from mCpG levels, across this period. We finally explore the relationship between DNAm patterns and development of brain-related phenotypes and find enriched heritability for many phenotypes within identified DNAm features. CONCLUSIONS: By profiling DNAm changes in NeuN-sorted neurons over the span of human cortical development, we identify novel, dynamic regions of DNAm that would be masked in homogenate DNAm data; expand on the relationship between CpG methylation, CpH methylation, and gene expression; and find enrichment particularly for neuropsychiatric diseases in genomic regions with cell type-specific, developmentally dynamic DNAm patterns.

Integrate imaging approach for minimally invasive and robotic procedures.

Over the past two decades, robotic and minimally invasive cardiac surgery has been continuously refined and is currently an alternative to traditional open-heart surgery for some patients. The parallel evolution of imaging modalities has made robotic surgery safer and more efficient. Here, we review the pre- and post-operative use of computed tomography (CT) in minimally invasive and robotic cardiac procedures.