Investigate the application of postoperative ctDNA-based molecular residual disease detection in monitoring tumor recurrence in patients with non-small cell lung cancer——A retrospective study of ctDNA

PurposeTo evaluate whether postoperative circulating tumor DNA (ctDNA) in plasma of patients with non-small cell lung cancer (NSCLC) can be used as a biomarker for early detection of molecular residual disease (MRD) and prediction of postoperative recurrence.MethodsThis study subjects were evaluated patients with surgical resected non-small cell lung cancer. All eligible patients underwent radical surgery operation followed by adjuvant therapy. Tumor tissue samples collected during operation were used to detect tumor mutation genes, and blood samples collected from peripheral veins after operation were used to collect ctDNA. Molecular residue disease (MRD) positive was defined as at least 1 true shared mutation identified in both the tumor sample and a plasma sample from the same patient was.ResultsPositive postoperatively ctDNA was associated with lower recurrence-free survival (RFS).The presence of MRD was a strong predictor of disease recurrence. The relative contribution of ctDNA-based MRD to the prediction of RFS is higher than all other clinicopathological variables, even higher than traditional TNM staging. In addition, MRD-positive patients who received adjuvant therapy had improved RFS compared to those who did not, the RFS of MRD-negative patients receiving adjuvant therapy was lower than that of patients not receiving adjuvant therapy.ConclusionsPost-operative ctDNA analysis is an effective method for recurrence risk stratification of NSCLC, which is beneficial to the management of patients with NSCLC

Rapid inactivation of human respiratory RNA viruses by deep ultraviolet irradiation from light-emitting diodes on a high-temperature-annealed AlN/Sapphire template

Efficient and eco-friendly disinfection of air-borne human respiratory RNA viruses is pursued in both public environment and portable usage. The AlGaN-based deep ultraviolet (DUV) light-emission diode (LED) has high practical potentials because of its advantages of variable wavelength, rapid sterilization, environmental protection, and miniaturization. Therefore, whether the emission wavelength has effects on the disinfection as well as whether the device is feasible to sterilize various respiratory RNA viruses under portable conditions is crucial. Here, we fabricate AlGaN-based DUV LEDs with different wavelength on high-temperature-annealed (HTA) AlN/Sapphire templates and investigate the inactivation effects for several respiratory RNA viruses. The AlN/AlGaN superlattices are employed between the template and upper n-AlGaN to release the strong compressive stress (SCS), improving the crystal quality and interface roughness. DUV LEDs with the wavelength of 256, 265, and 278 nm, corresponding to the light output power of 6.8, 9.6, and 12.5 mW, are realized, among which the 256 nm-LED shows the most potent inactivation effect in human respiratory RNA viruses, including SARS-CoV-2, influenza A virus (IAV), and human parainfluenza virus (HPIV), at a similar light power density (LPD) of ~0.8 mW/cm2 for 10 s. These results will contribute to the advanced DUV LED application of disinfecting viruses with high potency and broad spectrum in a portable and eco-friendly use

Immunomodulation by imiquimod in patients with high-risk primary melanoma.

Imiquimod is a synthetic Toll-like receptor 7 (TLR7) agonist approved for the topical treatment of actinic keratoses, superficial basal cell carcinoma, and genital warts. Imiquimod leads to an 80-100% cure rate of lentigo maligna; however, studies of invasive melanoma are lacking. We conducted a pilot study to characterize the local, regional, and systemic immune responses induced by imiquimod in patients with high-risk melanoma. After treatment of the primary melanoma biopsy site with placebo or imiquimod cream, we measured immune responses in the treated skin, sentinel lymph nodes (SLNs), and peripheral blood. Treatment of primary melanomas with 5% imiquimod cream was associated with an increase in both CD4+ and CD8+ T cells in the skin, and CD4+ T cells in the SLN. Most of the CD8+ T cells in the skin were CD25 negative. We could not detect any increases in CD8+ T cells specifically recognizing HLA-A(*)0201-restricted melanoma epitopes in the peripheral blood. The findings from this small pilot study demonstrate that topical imiquimod treatment results in enhanced local and regional T-cell numbers in both the skin and SLN. Further research into TLR7 immunomodulating pathways as a basis for effective immunotherapy against melanoma in conjunction with surgery is warranted

Visualized analysis of developing trends and hot topics in natural disaster research

<div><p>This study visualized and analyzed the developing trends and hot topics in natural disaster research. 19694 natural disaster-related articles (January 1900 to June 2015) are indexed in the Web of Science database. The first step in this study is using complex networks to visualize and analyze these articles. CiteSpace and Gephi were employed to generate a countries collaboration network and a disciplines collaboration network, and then attached hot topics to countries and disciplines, respectively. The results show that USA, China, and Italy are the three major contributors to natural disaster research. “Prediction model”, “social vulnerability”, and “landslide inventory map” are three hot topics in recent years. They have attracted attention not only from large countries like China but also from small countries like Panama and Turkey. Comparing two hybrid networks provides details of natural disaster research. Scientists from USA and China use image data to research earthquakes. Indonesia and Germany collaboratively study tsunamis in the Indian Ocean. However, Indonesian studies focus on modeling and simulations, while German research focuses on early warning technology. This study also introduces an activity index (AI) and an attractive index (AAI) to generate time evolution trajectories of some major countries from 2000 to 2013 and evaluate their trends and performance. Four patterns of evolution are visible during this 14-year period. China and India show steadily rising contributions and impacts, USA and England show relatively decreasing research efforts and impacts, Japan and Australia show fluctuating activities and stable attraction, and Spain and Germany show fluctuating activities and increasing impacts.</p></div

A collaboration network of natural disasters of 160 nodes and 157 links.

<p>Circular nodes represent countries/regions. The size of a circle is in proportion to the number of literatures of the country. The colors of rings of a circle are corresponding to the year. The purple rims of circles represent the high betweenness centralities.</p

Leveraging Multisource Heterogeneous Data for Financial Risk Prediction: A Novel Hybrid-Strategy-Based Self-Adaptive Method

Emerging phenomena of ubiquitous multisource data offer promising avenues for making breakthroughs in financial risk prediction. While most existing methods for financial risk prediction are based on a single information source, which may not adequately capture various complex factors that jointly influence financial risks, we propose a hybrid-strategy-based self-adaptive method to effectively leverage heterogeneous soft information drawn from a variety of sources. The method uses a proposed new feature- sparsity learning method to adaptively integrate multisource heterogeneous soft features with hard features and a proposed improved evidential reasoning rule to adaptively aggregate base classifier predictions, thereby alleviating both the declarative bias and the procedural bias of the learning process. Evaluation in two cases at the individual level (concerning borrowers at a P2P lending platform) and the company level (concerning listed companies in the Chinese stock market) showed that, compared with relying solely on hard features, effectively incorporating multisource heterogeneous soft features using our proposed method enabled earlier prediction of financial risks with desirable performance