Local generation of hydrogen for enhanced photothermal therapy.

By delivering the concept of clean hydrogen energy and green catalysis to the biomedical field, engineering of hydrogen-generating nanomaterials for treatment of major diseases holds great promise. Leveraging virtue of versatile abilities of Pd hydride nanomaterials in high/stable hydrogen storage, self-catalytic hydrogenation, near-infrared (NIR) light absorption and photothermal conversion, here we utilize the cubic PdH0.2 nanocrystals for tumour-targeted and photoacoustic imaging (PAI)-guided hydrogenothermal therapy of cancer. The synthesized PdH0.2 nanocrystals have exhibited high intratumoural accumulation capability, clear NIR-controlled hydrogen release behaviours, NIR-enhanced self-catalysis bio-reductivity, high NIR-photothermal effect and PAI performance. With these unique properties of PdH0.2 nanocrystals, synergetic hydrogenothermal therapy with limited systematic toxicity has been achieved by tumour-targeted delivery and PAI-guided NIR-controlled release of bio-reductive hydrogen as well as generation of heat. This hydrogenothermal approach has presented a cancer-selective strategy for synergistic cancer treatment

High-optical-quality nanosphere lithographically formed InGaAs quantum dots using molecular beam epitaxy assisted GaAs mass transport and overgrowth

Optically active, highly uniform, cylindrical InGaAs quantum dot ͑QD͒ arrays have been fabricated using nanosphere lithography combined with Bromine ion-beam-assisted etching and molecular beam epitaxy ͑MBE͒-assisted GaAs mass transport. Previously fabricated QD nanopillar arrays showed significant degradation of optical properties due to the etch damage. Here, a novel mass transport process in a Riber 3200 was performed to encapsulate the lithographically defined InGaAs disk QDs in a GaAs matrix, resulting in the passivation of the etch-damaged QD sidewall layer. Photoluminescence emission intensity following the mass transport process increased by a magnitude of 4-10 as compared to that from unprocessed nanopillar sample. In addition, a PL peak energy redshift was observed after mass transport, presumably due to the decrease in the lateral barrier potential from vacuum to GaAs, as well as the elimination of the depletion layer. Furthermore, the mass transport process in the high vacuum MBE environment enables GaAs overgrowth with few defects and dislocations following mass transport for surface planarization. PL emission intensity increased by an additional factor of 4 following GaAs overgrowth, bringing the QD intensity to 1 2 of that of the original single quantum well. Thus, the potential of the MBE-assisted mass transport process has been demonstrated to fabricate high optical quality InGaAs quantum dots encapsulated in a GaAs matrix for device applications

FATS is a transcriptional target of p53 and associated with antitumor activity

Frequent mutations of p53 in human cancers exemplify its crucial role as a tumor suppressor transcription factor, and p21, a transcriptional target of p53, plays a central role in surveillance of cell-cycle checkpoints. Our previous study has shown that FATS stabilize p21 to preserve genome integrity. In this study we identified a novel transcript variant of FATS (GenBank: GQ499374) through screening a cDNA library from mouse testis, which uncovered the promoter region of mouse FATS. Mouse FATS was highly expressed in testis. The p53-responsive elements existed in proximal region of both mouse and human FATS promoters. Functional study indicated that the transcription of FATS gene was activated by p53, whereas such effect was abolished by site-directed mutagenesis in the p53-RE of FATS promoter. Furthermore, the expression of FATS increased upon DNA damage in a p53-dependent manner. FATS expression was silent or downregulated in human cancers, and overexpression of FATS suppressed tumorigenicity in vivo independently of p53. Our results reveal FATS as a p53-regulated gene to monitor genomic stability

Identification of Susceptibility Pathways for the Role of Chromosome 15q25.1 in Modifying Lung Cancer Risk

Genome-wide association studies (GWAS) identified the chromosome 15q25.1 locus as a leading susceptibility region for lung cancer. However, the pathogenic pathways, through which susceptibility SNPs within chromosome 15q25.1 affects lung cancer risk, have not been explored. We analyzed three cohorts with GWAS data consisting 42,901 individuals and lung expression quantitative trait loci (eQTL) data on 409 individuals to identify and validate the underlying pathways and to investigate the combined effect of genes from the identified susceptibility pathways. The KEGG neuroactive ligand receptor interaction pathway, two Reactome pathways, and 22 Gene Ontology terms were identified and replicated to be significantly associated with lung cancer risk, with P values less than 0.05 and FDR less than 0.1. Functional annotation of eQTL analysis results showed that the neuroactive ligand receptor interaction pathway and gated channel activity were involved in lung cancer risk. These pathways provide important insights for the etiology of lung cancer

Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes.

Although several lung cancer susceptibility loci have been identified, much of the heritability for lung cancer remains unexplained. Here 14,803 cases and 12,262 controls of European descent were genotyped on the OncoArray and combined with existing data for an aggregated genome-wide association study (GWAS) analysis of lung cancer in 29,266 cases and 56,450 controls. We identified 18 susceptibility loci achieving genome-wide significance, including 10 new loci. The new loci highlight the striking heterogeneity in genetic susceptibility across the histological subtypes of lung cancer, with four loci associated with lung cancer overall and six loci associated with lung adenocarcinoma. Gene expression quantitative trait locus (eQTL) analysis in 1,425 normal lung tissue samples highlights RNASET2, SECISBP2L and NRG1 as candidate genes. Other loci include genes such as a cholinergic nicotinic receptor, CHRNA2, and the telomere-related genes OFBC1 and RTEL1. Further exploration of the target genes will continue to provide new insights into the etiology of lung cancer

Genome-Wide Association Study of Lung Adenocarcinoma in East Asia and Comparison With a European Population

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages