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This study integrates the daily intercity migration data with the classic Susceptible-Exposed-Infected-Removed (SEIR) model to construct a new model suitable for describing the dynamics of epidemic spreading of Coronavirus Disease 2019 (COVID-19) in China. Daily intercity migration data for 367 cities in China were collected from Baidu Migration, a mobile-app based human migration tracking data system. Early outbreak data of infected, recovered and death cases from official source (from January 24 to February 16, 2020) were used for model fitting. The set of model parameters obtained from best data fitting using a constrained nonlinear optimisation procedure was used for estimation of the dynamics of epidemic spreading in the following months. The work was completed on February 19, 2020. Our results showed that the number of infections in most cities in China would peak between mid February to early March 2020, with about 0.8%, less than 0.1% and less than 0.01% of the population eventually infected in Wuhan, Hubei Province and the rest of China, respectively. Moreover, for most cities outside and within Hubei Province (except Wuhan), the total number of infected individuals is expected to be less than 300 and 4000, respectively.

This work applies a data-driven coding method for prediction of the COVID-19 spreading profile in any given population that shows an initial phase of epidemic progression. Based on the historical data collected for COVID-19 spreading in 367 cities in China and the set of parameters of the augmented Susceptible-Exposed-Infected-Removed (SEIR) model obtained for each city, a set of profile codes representing a variety of transmission mechanisms and contact topologies is formed. By comparing the data of an early outbreak of a given population with the complete set of historical profiles, the best fit profiles are selected and the corresponding sets of profile codes are used for prediction of the future progression of the epidemic in that population. Application of the method to the data collected for South Korea, Italy and Iran shows that peaks of infection cases are expected to occur before mid April, the end of March and the end of May 2020, and that the percentage of population infected in each city or region will be less than 0.01%, 0.5% and 0.5%, for South Korea, Italy and Iran, respectively.

At the end of 2019, a new coronavirus (COVID-19) epidemic has triggered global public health concern. Here, a model integrating the daily intercity migration network, which constructed from real-world migration records and the Susceptible–Exposed–Infected–Removed model, is utilized to predict the epidemic spreading of the COVID-19 in more than 300 cities in China. However, the model has more than 1800 unknown parameters, which is a challenging task to estimate all unknown parameters from historical data within a reasonable computation time. In this article, we proposed a pseudocoevolutionary simulated annealing (SA) algorithm for identifying these unknown parameters. The large volume of unknown parameters of this model is optimized through three procedures co-adapted SA-based optimization processes, respectively. Our results confirm that the proposed method is both efficient and robust. Then, we use the identified model to predict the trends of the epidemic spreading of the COVID-19 in these cities. We find that the number of infections in most cities in China has reached their peak from February 29, 2020, to March 15, 2020. For most cities outside Hubei province, the total number of infected individuals would be less than 100, while for most cities in Hubei province (exclude Wuhan), the total number of infected individuals would be less than 3000.

The coronavirus disease (COVID-19) began to spread in mid-December 2019 from Wuhan, China, to most provinces in China and over 200 other countries through an active travel network. Limited by the ability of the country or city to perform tests, the officially reported number of confirmed cases is expected to be much smaller than the true number of infected cases. This study aims to develop a new susceptible-exposed-infected-confirmed-removed (SEICR) model for predicting the spreading progression of COVID-19 with consideration of intercity travel and the difference between the number of confirmed cases and actual infected cases, and to apply the model to provide a realistic prediction for the United States and Japan under different scenarios of active intervention. The model introduces a new state variable corresponding to the actual number of infected cases, integrates intercity travel data to track the movement of exposed and infected individuals among cities, and allows different levels of active intervention to be considered so that a realistic prediction of the number of infected individuals can be performed. Moreover, the model generates future progression profiles for different levels of intervention by setting the parameters relative to the values found from the data fitting. By fitting the model with the data of the COVID-19 infection cases and the intercity travel data for Japan (January 15 to March 20, 2020) and the United States (February 20 to March 20, 2020), model parameters were found and then used to predict the pandemic progression in 47 regions of Japan and 50 states (plus a federal district) in the United States. The model revealed that, as of March 19, 2020, the number of infected individuals in Japan and the United States could be 20-fold and 5-fold as many as the number of confirmed cases, respectively. The results showed that, without tightening the implementation of active intervention, Japan and the United States will see about 6.55% and 18.2% of the population eventually infected, respectively, and with a drastic 10-fold elevated active intervention, the number of people eventually infected can be reduced by up to 95% in Japan and 70% in the United States. The new SEICR model has revealed the effectiveness of active intervention for controlling the spread of COVID-19. Stepping up active intervention would be more effective for Japan, and raising the level of public vigilance in maintaining personal hygiene and social distancing is comparatively more important for the United States.

The C-reactive protein-to-albumin ratio (CAR) is an emerging biomarker linked to cardiovascular disease. However, its role in predicting outcomes among acute decompensated heart failure (ADHF) patients, particularly when stratified by glycemic status, remains poorly defined. This retrospective study included 1,494 consecutive ADHF patients admitted between 2018 and 2023, with the primary endpoint being major adverse cardiac and cerebrovascular events (MACCEs). Patients were stratified into low- and high-CAR groups using an optimal cutoff of 0.29, derived from maximally selected rank statistics. To assess the relationship between CAR and MACCEs in different glucose metabolism states, multivariable Cox proportional hazards models, restricted cubic splines (RCS), and Kaplan-Meier survival curves were used. During a median follow-up of 528 days, 565 patients (37.8%) experienced MACCEs. After adjusting for potential confounders, higher CAR levels were significantly associated with an increased risk of MACCEs (Model 3: hazard ratio [HR]: 1.45, 95% confidence interval [CI]: 1.19–1.77, p  < 0.001). When stratified by glycemic status, CAR as a robust predictor in diabetic patients (Model 3 HR: 1.91, 95% CI: 1.41–2.58, p  < 0.001), whereas no significant associations were observed in prediabetic or normoglycemic individuals (all p  > 0.05). In conclusion, CAR demonstrates independent prognostic value in ADHF patients with concomitant diabetes, highlighting its potential as a stratification tool for personalized risk assessment and therapeutic optimization in this high-risk population.

Background To explore the diagnostic value of Caprini risk assessment model (2005) combined with D-dimer for deep vein thrombosis, and to exclude patients with low incidence of thrombosis who might not need anticoagulation after surgery. Methods A total of 171 colorectal cancer patients who underwent surgery from January 2022 to August 2022 were enrolled in this study. Caprini risk assessment model was used to evaluate patients the day before surgery, and full-length venous ultrasonography of lower extremity was used to assess whether patients had thrombosis one day before surgery and the sixth day after surgery. The value of D-dimer was measured by enzyme-linked immunosorbent assays on the first day after surgery, and clinical data of patients were collected during hospitalization. Results A total of 171 patients were divided into IPC Group and IPC + LMWH Group according to whether low molecular weight heparin (LMWH) were used to prevent thrombus after surgery. Eventually, 17.6% (15/85) patients in IPC Group and 7% (6/86) patients in IPC + LMWH Group developed DVT. Through separate analysis of IPC Group, it is found that Caprini score and D-dimer were independent risk factors for DVT (Caprini OR 3.39 [95% CI 1.38–8.32]; P  = 0.008, D-Dimer OR 6.142 [95% CI 1.209–31.187]; P  = 0.029). The area under ROC curve of Caprini risk assessment model is 0.792 (95% CI 0.69–0.945, P  < 0.01), the cut-off value is 9.5, and the area under ROC curve of D-dimer is 0.738 (95%CI 0.555–0.921, P  < 0.01), the cut-off value is 0.835 μg/mL, and the area under the ROC curve was 0.865 (95% CI 0.754–0.976, P  < 0.01) when both of them were combined. Based on decision curve analysis, it is found that Caprini risk assessment model combined with D-dimer can benefit patients more. All patients are divided into four groups. When Caprini score < 10 and D-dimer < 0.835 μg/mL, only 1.23% (1/81) of patients have thrombosis and LMWH has little significance. When Caprini score > 10 and D-dimer > 0.835 μg/mL, the incidence of DVT is 38.7% (12/31) and LMWH should be considered. Conclusions The Caprini risk assessment model and D-dimer can provide more accurate risk stratification for patients after laparoscopic radical resection of colorectal cancer.

Erythropoietin-producing hepatocellular receptor A2 (EphA2) is a key member of the receptor tyrosine kinase (RTK) family, while YES Proto-Oncogene 1 (YES1) is a non-receptor tyrosine kinase (nRTK) and annexin A2 (ANXA2) belongs to the calcium-dependent phospholipid-binding protein family annexins. Here, we show that EphA2, YES1, and ANXA2 form a signal axis, in which YES1 activated by EphA2 phosphorylates ANXA2 at Tyr24 site, leading to ANXA2 activation and increased ANXA2 nuclear distribution in gastric cancer (GC) cells. Overexpression (OE) of YES1 increases, while knockdown (KD) of YES1 or ANXA2 decreases GC cell invasion and migration in vitro and tumor growth in mouse models. Reexpression of wildtype (WT) rather than mutant ANXA2 (Tyr24F) in ANXA2 knockdown (ANXA2-KD) GC cells restores YES1-induced cell invasion and migration, while neither WT nor mutant ANXA2 (Tyr24F) can restore cell invasion and migration in YES1-KD GC cells. In addition, the activation of EphA2–YES1–ANXA2 pathway is correlated with poor prognosis. Thus, our results establish EphA2–YES1–ANXA2 axis as a novel pathway that drives GC invasion and metastasis, targeting this pathway would be an efficient way for the treatment of GC.

Tumor deposits in colon cancer are related to poor prognosis, whereas the prognostic power of tumor deposits in combination with lymph node metastasis (LNM) is controversial. This study aimed to compare the overall survival between LNM alone and LNM in combination with tumor deposits, and to verify whether the number of tumor deposits can be considered LNM in patients with both LNM and tumor deposits in stage III colon cancer by propensity score matching (PSM). Patients carrying resected stage III adenocarcinoma of colon cancer were identified from the Surveillance, Epidemiology, and End Results database (2010–2015). The Kaplan-Meier method, Cox proportional hazard models and PSM were used. On the whole, 23,168 patients (20,451 (88.3%) with only LNM and 2,717 (11.7%) with both LNM and tumor deposits) were selected. After undergoing PSM, patients with both LNM and tumor deposits showed worse overall survival (hazard ratio = 1.33, 95% confidence interval: 1.20–1.47, P < 0.001). After the number of tumor deposits was added with that of positive regional lymph nodes, patients with both LNM and tumor deposits seemed to have prognostic implications similar to those with LNM alone (hazard ratio = 1.02, 95% confidence interval: 0.93–1.12, P = 0.66). The simultaneous presence of LNM and tumor deposits, as compared with the presence of only LNM, had an association with a worse outcome. Tumor deposits should be considered as LNM in patients with both tumor deposits and LNM in stage III colon cancer.