Patients achieved pCR during neoadjuvant chemotherapy had better outcome than adjuvant chemotherapy setting in breast cancer: A comparative study.

PURPOSE: Pathological complete response(pCR) during neoadjuvant chemotherapy(NAC) has been proposed as a predictor for better prognosis in breast cancer. However, few studies compare the outcomes of patients receiving NAC and adjuvant chemotherapy(AC). METHODS: We retrospectively matched the patients who received NAC(N = 462) and AC(N = 462) by age, time of diagnosis, and primary clinical stage using the propensity score match in breast cancer patients treated in Sir Run Run Shaw Hospital with the median follow up of 67 months. Death from breast cancer and recurrence were used as endpoints. A multivariable Cox models were used to estimate the hazard ratios for breast-cancer specific survival (BCSS) and DFS. A multivariable logistic regression model was simulated to predict pCR. RESULTS: In patients who received NAC, 18.0%(83/462) patients achieved pCR, while the rest of the patients did not. pCR subgroup demonstrated significant better BCSS and DFS than patients receiving AC(BCSS: HR = 0.39, 95% CI:0.12-0.93, P = 0.03; DFS: HR = 0.16, 95%CI 0.009-0.73, P = 0.013) and non-pCR patients(BCSS: HR = 0.32, 95%CI 0.10-0.77, P = 0.008; DFS: HR = 0.12, 95%CI 0.007-0.55, P = 0.002). Patients who received AC demonstrated insignificant survival compared to non-pCR patients(BCSS: HR= 0.82, 95%CI 0.62-1.10, P = 0.19; DFS: HR = 0.75, 95%CI 0.53-1.07, P = 0.12). Patients with AC had significant better DFS than non-pCR patients(HR = 0.33, 95% CI 0.10-0.94, P = 0.04) in luminal B Her2+ patients. More NAC cycles(>2), TNBC, lower cT stage, and mixed histology indicate higher possibility of pCR(AUC = 0.89). CONCLUSION: pCR patients with NAC indicated better prognosis than patients receiving AC or non-pCR patients from NAC. The timing of chemotherapy may need carefully pondering in luminal B Her2+ patients.

Impact of Wuhan lockdown on the spread of COVID-19 in China: a study based on the data of population mobility.

This study aimed to quantitatively assess the effectiveness of the Wuhan lockdown measure on controlling the spread of coronavirus diesase 2019 (COVID-19). : Firstly，estimate the daily new infection rate in Wuhan before January 23，2020 when the city went into lockdown by consulting the data of Wuhan population mobility and the number of cases imported from Wuhan in 217 cities of Mainland China. Then estimate what the daily new infection rate would have been in Wuhan from January 24 to January 30th if the lockdown measure had been delayed for 7 days，assuming that the daily new infection in Wuhan after January 23 increased in a high，moderate and low trend respectively (using exponential, linear and logarithm growth models). Based on that，calculate the number of infection cases imported from Wuhan during this period. Finally，predict the possible impact of 7-day delayed lockdown in Wuhan on the epidemic situation in China using the susceptible-exposed-infectious-removed (SEIR) model. : The daily new infection rate in Wuhan was estimated to be 0.021%，0.026%，0.029%，0.033% and 0.070% respectively from January 19 to January 23. And there were at least 20 066 infection cases in Wuhan by January 23，2020. If Wuhan lockdown measure had been delayed for 7 days，the daily new infection rate on January 30 would have been 0.335% in the exponential growth model，0.129% in the linear growth model，and 0.070% in the logarithm growth model. Correspondingly，there would have been 32 075，24 819 and 20 334 infection cases travelling from Wuhan to other areas of Mainland China，and the number of cumulative confirmed cases as of March 19 in Mainland China would have been 3.3-3.9 times of the officially reported number. Conclusions: Timely taking city-level lockdown measure in Wuhan in the early stage of COVID-19 outbreak is essential in containing the spread of the disease in China.

Impact of socioeconomic status，population mobility and control measures on COVID-10 development in major cities of China.

:To evaluate the impact of socioeconomic status，population mobility，prevention and control measures on the early-stage coronavirus disease 2019 (COVID-19) development in major cities of China. : The rate of daily new confirmed COVID-19 cases in the 51 cities with the largest number of cumulative confirmed cases as of February 19，2020 (except those in Hubei province) were collected and analyzed using the time series cluster analysis. It was then assessed according to three aspects，that is, socioeconomic status，population mobility，and control measures for the pandemic. : According to the analysis on the 51 cities，4 development patterns of COVID-19 were obtained，including a high-incidence pattern (in Xinyu)，a late high-incidence pattern (in Ganzi)，a moderate incidence pattern (in Wenzhou and other 12 cities)，and a low and stable incidence pattern (in Hangzhou and other 35 cities). Cities with different types and within the same type both had different scores on the three aspects. : There were relatively large difference on the COVID-19 development among different cities in China，possibly affected by socioeconomic status，population mobility and prevention and control measures that were taken. Therefore，a timely public health emergency response and travel restriction measures inside the city can interfere the development of the pandemic. Population flow from high risk area can largely affect the number of cumulative confirmed cases.

Predicting COVID-19 epidemiological trend by applying population mobility data in two-stage modeling.

:To predict the epidemiological trend of coronavirus disease 2019 (COVID-19) by mathematical modeling based on the population mobility and the epidemic prevention and control measures. : As of February 8，2020，the information of 151 confirmed cases in Yueqing，Zhejiang province were obtained，including patients' infection process，population mobility between Yueqing and Wuhan，etc. To simulate and predict the development trend of COVID-19 in Yueqing, the study established two-stage mathematical models，integrating the population mobility data with the date of symptom appearance of confirmed cases and the transmission dynamics of imported and local cases. : It was found that in the early stage of the pandemic，the number of daily imported cases from Wuhan (using the date of symptom appearance) was positively associated with the number of population travelling from Wuhan to Yueqing on the same day and 6 and 9 days before that. The study predicted that the final outbreak size in Yueqing would be 170 according to the number of imported cases estimated by consulting the population number travelling from Wuhan to Yueqing and the susceptible-exposed-infectious-recovered (SEIR) model; while the number would be 165 if using the reported daily number of imported cases. These estimates were close to the 170，the actual monitoring number of cases in Yueqing as of April 27，2020. : The two-stage modeling approach used in this study can accurately predict COVID-19 epidemiological trend.