Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

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

Advances in Liquid Biopsy and its Clinical Application in the Diagnosis and Treatment of Non-small Cell Lung Cancer

With the advances of technology, great progresses have been made in liquid biopsy in recent years. Liquid biopsy is currently playing a more and more important role in early diagnosis and treatment of cancer. Compared with traditional tissue biopsy, liquid biopsy is more popular in clinical practice due to its non-invasiveness, convenience and high repeatability. It has huge potential in the future. This review introduces circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) as the most important objects in liquid biopsy, mainly focusing on their history, biological characteristics, detection technologies, limitations and applications in non-small cell lung cancer

Optimum Design and Performance Analysis of Superconducting Cable with Different Conductor Layout

Compared with the traditional cable, the high-temperature superconducting (HTS) cable has the advantages of low loss and large capacity transmission. At present, the research on HTS cables mainly focuses on the calculation of AC loss, the performance under specific working conditions and cooling system design. Relatively little research has been carried out on the basic design and overall layout optimization of the cables. In this paper, an HTS cable with a rated current of 4 kA was designed. Firstly, according to the selected superconducting cable parameters, the body design of cables with different structures was carried out and the corresponding finite element models were built. Then, the performance analysis of HTS cables with different layouts was carried out based on the proposed cable performance evaluation indicators and the CORC double-layer structure was determined as the scheme of this cable. Finally, the AC loss of the cable with this topology was calculated to be 9.81 J/m under rated conditions. The cooling system can ensure the safe operation of the cable in the rated temperature range

Risk stratification model for patients with stage I invasive lung adenocarcinoma based on clinical and pathological predictors

Background: The aim of this study was to propose a new kind of pathological classification and further establish a prognostic model for resected stage I invasive adenocarcinoma (IADC). Methods: Clinicopathological data were collected from 2 hospitals. The new proposed pathological reclassification was defined according to certain subtype instead of a predominant one. Survival curves were plotted by Kaplan-Meier analysis. Cox regressions were analyzed for recurrence-free survival (RFS) and overall survival (OS), through which prognostic scores and stratification models were established. The comparison between risk models and the eighth edition of tumor, node, metastasis (TNM) classification was conducted through receiver operating characteristic curves (ROC), as identified by the area under the curve (AUC) and z test. Results: In all, 1,196 patients were enrolled. At multivariable analysis, solid and micropapillary of the new pathological reclassification, along with stage IA3 and IB were independent predictors for poorer RFS. Stage IB and smoking status significantly indicated worse OS. After normalization and standardization of log-hazard ratio (HR), personalized scores were calculated and the risk stratifications with 3 risk groups were generated. Compared with TNM classification, the risk model of RFS showed advantage over early-recurrence prediction (1-year: 0.653 vs. 0.556, P=0.033; 3-year: 0.663 vs. 0.076, P=0.008). No marked difference was observed in long-term RFS or OS. Conclusions: Considering the harboring of certain patterns may be a new concept in adenocarcinoma classification. The risk stratification model based on this pathological classification and the eighth TNM classification showed remarkable superiority over TNM alone in predicting early recurrence of stage I adenocarcinoma. However, TNM classification remained valuable for long-term recurrence and survival prediction

Gender Differences and Influencing Factors in Specialty Choices: Findings From One Medical School in China.

Background: Gender plays a significant role in the selection of medical specialty. Few studies have been conducted to explore the impact of gender differences on specialty choosing among Chinese medical students. Methods: The specialty choices of 648 students from six consecutive classes in an 8-year MD program were collected and compared between male and female students. A total of 110 students from one graduating class were surveyed by a questionnaire covering 22 career influencing factors. Each factor has a scale of zero to three (zero = no influence, one = mild influence, two = moderate influence, and three = strong influence). Results: Statistically significant gender differences were observed in 10 out of 16 specialties. Most male students limited their specialty choices to surgery (64%), internal medicine (12%), and orthopedics (12%), compared with a relatively diversified pattern in female students. For male students, the top three influencing factors were personal interest, future job prospects for the chosen specialty, and job opportunity in academic medicine. The strongest influencing factors of females were personal interest, specialty-specific knowledge and skills, and the sense of achievement. The expected salary was ranked among the top 10 influencing factors in male but not in females, while the work-life balance was ranked among the top 10 factors in females but not in males. Conclusion: There is a significant gender difference regarding specialty choices among Chinese medical students. Career coaching is needed to help students in their specialty choosing process