The relationship between time to a high COVID-19 response level and timing of peak daily incidence: an analysis of governments' Stringency Index from 148 countries.

BACKGROUND: The transmission dynamics and severity of coronavirus disease 2019 (COVID-19) pandemic is different across countries or regions. Differences in governments' policy responses may explain some of these differences. We aimed to compare worldwide government responses to the spread of COVID-19, to examine the relationship between response level, response timing and the epidemic trajectory. METHODS: Free publicly-accessible data collected by the Coronavirus Government Response Tracker (OxCGRT) were used. Nine sub-indicators reflecting government response from 148 countries were collected systematically from January 1 to May 1, 2020. The sub-indicators were scored and were aggregated into a common Stringency Index (SI, a value between 0 and 100) that reflects the overall stringency of the government's response in a daily basis. Group-based trajectory modelling method was used to identify trajectories of SI. Multivariable linear regression models were used to analyse the association between time to reach a high-level SI and time to the peak number of daily new cases. RESULTS: Our results identified four trajectories of response in the spread of COVID-19 based on when the response was initiated: before January 13, from January 13 to February 12, from February 12 to March 11, and the last stage-from March 11 (the day WHO declared a pandemic of COVID-19) on going. Governments' responses were upgraded with further spread of COVID-19 but varied substantially across countries. After the adjustment of SI level, geographical region and initiation stages, each day earlier to a high SI level (SI > 80) from the start of response was associated with 0.44 (standard error: 0.08, P < 0.001, R2 = 0.65) days earlier to the peak number of daily new case. Also, each day earlier to a high SI level from the date of first reported case was associated with 0.65 (standard error: 0.08, P < 0.001, R2 = 0.42) days earlier to the peak number of daily new case. CONCLUSIONS: Early start of a high-level response to COVID-19 is associated with early arrival of the peak number of daily new cases. This may help to reduce the delays in flattening the epidemic curve to the low spread level.

Omipalisib Inhibits Esophageal Squamous Cell Carcinoma Growth Through Inactivation of Phosphoinositide 3-Kinase (PI3K)/AKT/Mammalian Target of Rapamycin (mTOR) and ERK Signaling.

BACKGROUND Esophageal squamous cell carcinoma (ESCC) is a life-threatening digestive tract malignancy with no known curative treatment. This study aimed to investigate the antineoplastic effects of omipalisib and its underlying molecular mechanisms in ESCC using a high throughput screen. MATERIAL AND METHODS MTT assay and clone formation were used to determine cell viability and proliferation. Flow cytometry was conducted to detect cell cycle distribution and apoptosis. Global gene expression and mRNA expression levels were determined by RNA sequencing and real-time PCR, respectively. Protein expression was evaluated in the 4 ESCC cell lines by Western blot analysis. Finally, a xenograft nude mouse model was used to evaluate the effect of omipalisib on tumor growth in vivo. RESULTS In the pilot screening of a 1404-compound library, we demonstrated that omipalisib markedly inhibited cell proliferation in a panel of ESCC cell lines. Mechanistically, omipalisib induced G0/G1 cell cycle arrest and apoptosis. RNA-seq, KEGG, and GSEA analyses revealed that the PI3K/AKT/mTOR pathway is the prominent target of omipalisib in ESCC cells. Treatment with omipalisib decreased expression of p-AKT, p-4EBP1, p-p70S6K, p-S6, and p-ERK, therefore disrupting the activation of PI3K/AKT/mTOR and ERK signaling. In the nude mouse xenograft model, omipalisib significantly suppressed the tumor growth in ESCC tumor-bearing mice without obvious adverse effects. CONCLUSIONS Omipalisib inhibited the proliferation and growth of ESCC by disrupting PI3K/AKT/mTOR and ERK signaling. The present study supports the rationale for using omipalisib as a therapeutic approach in ESCC patients. Further clinical studies are needed.

Wide Gastric Conduit Increases the Risk of Benign Anastomotic Stricture After Esophagectomy.

BACKGROUND: To identify the association between the width of the gastric conduit and the benign anastomotic stricture (BAS) after esophagectomy with end-to-side cervical anastomosis for esophageal cancer. METHODS: Patients with esophageal cancer who underwent esophagectomy between July 2013 and July 2014 were included in this study. The gastric conduit was used for reconstruction in all patients and end-to-side cervical anastomosis were performed using a circular stapler. The patients were divided into a narrow group (3-5 cm) and a wide group (>5 cm) based on the gastric conduit width. Univariate and multivariate logistic regressions were used to analyze the possible factors (patients' age, gender, preoperative comorbidities, neoadjuvant chemotherapy, gastric conduit width, anastomotic leakage) that could affect the incidence of BAS. RESULTS: Two-hundred and one patients were included in this study. The median follow-up period was 29 months (17-58 months). Seven cases (3.5%) showed anastomotic leakage in the postoperative period and 38 patients (18.9%) developed BAS; all within the first year of follow-up. In univariate analysis, the width of the gastric conduit was the only risk factor for the development of BAS (odds ratio [OR] = 3.36, P = .005). In multivariate logistic regression analysis, the wide group was an independent significant risk factor for the development of BAS developing compared with the narrow group (OR = 2.84, P = .02). CONCLUSIONS: A wide gastric conduit width (>5 cm) is an independent risk factor for the development of BAS after esophagectomy and stapled cervical end-to-side anastomosis for esophageal cancer.

Association of Overlapped and Un-overlapped Comorbidities with COVID-19 Severity and Treatment Outcomes: A Retrospective Cohort Study from Nine Provinces in China.

OBJECTIVE: Several COVID-19 patients have overlapping comorbidities. The independent role of each component contributing to the risk of COVID-19 is unknown, and how some non-cardiometabolic comorbidities affect the risk of COVID-19 remains unclear. METHODS: A retrospective follow-up design was adopted. A total of 1,160 laboratory-confirmed patients were enrolled from nine provinces in China. Data on comorbidities were obtained from the patients' medical records. Multivariable logistic regression models were used to estimate the odds ratio ( OR) and 95% confidence interval (95% CI) of the associations between comorbidities (cardiometabolic or non-cardiometabolic diseases), clinical severity, and treatment outcomes of COVID-19. RESULTS: Overall, 158 (13.6%) patients were diagnosed with severe illness and 32 (2.7%) had unfavorable outcomes. Hypertension (2.87, 1.30-6.32), type 2 diabetes (T2DM) (3.57, 2.32-5.49), cardiovascular disease (CVD) (3.78, 1.81-7.89), fatty liver disease (7.53, 1.96-28.96), hyperlipidemia (2.15, 1.26-3.67), other lung diseases (6.00, 3.01-11.96), and electrolyte imbalance (10.40, 3.00-26.10) were independently linked to increased odds of being severely ill. T2DM (6.07, 2.89-12.75), CVD (8.47, 6.03-11.89), and electrolyte imbalance (19.44, 11.47-32.96) were also strong predictors of unfavorable outcomes. Women with comorbidities were more likely to have severe disease on admission (5.46, 3.25-9.19), while men with comorbidities were more likely to have unfavorable treatment outcomes (6.58, 1.46-29.64) within two weeks. CONCLUSION: Besides hypertension, diabetes, and CVD, fatty liver disease, hyperlipidemia, other lung diseases, and electrolyte imbalance were independent risk factors for COVID-19 severity and poor treatment outcome. Women with comorbidities were more likely to have severe disease, while men with comorbidities were more likely to have unfavorable treatment outcomes.

Dose titration model and correlative factors analysis in Chinese patients with type-2 diabetes on basal insulin - results from an Observational Registry of Basal Insulin Treatment study.

INTRODUCTION: This study evaluates an insulin dose titration model and factors that impact insulin dose adjustment in Chinese adults with type-2 diabetes, who receive basal insulin in real-world settings. MATERIAL AND METHODS: A total of 19,894 patients from the ORBIT study were included. These patients were divided into four groups, according to the type of insulin dose adjustment: no insulin titration (group A), self-titration (group B), physician-led insulin titration (group C), and combined physician and patient-led insulin titration (group D). Data were collected and compared at baseline and after six months of treatment. RESULTS: A total of 12,865 patients completed the visits and were included in the analysis. Among these patients, 3187 (24.8%), 1971 (15.3%), 5165 (40.1%), and 2542 (19.8%) patients were included in groups A, B, C, and D, respectively. The multivariate logistic regression analysis revealed that the duration of diabetes, body mass index, microvascular complications, inpatient days, HbA1C level, and self-monitoring of blood glucose (SMBG) were positively correlated with insulin titration in group B, C, and D, compared with group A. The number of inpatient days and outpatient visits were positively correlated with dose adjustment for physician-led titration, while this was negatively correlated for self-titration. Self-titration encouraged by physicians and home blood glucose monitoring were positively correlated with self-titration and the combined physician and patient-led titration. CONCLUSIONS: High HbA1C level, SMBG, long disease duration, microvascular complications, and the encouragement of physicians while initiating insulin use prompt patients to perform dose adjustments in real-world settings.

[Evaluating the effect of preventive medicine for residents living around mosquito breeding water during rest period of malaria].

OBJECTIVE: To evaluate the effect of preventive medicine for residents living around mosquito breeding water during rest period of malaria by delimiting a certain range. METHOD: The study adopted the stratified cluster random sampling method to select subjects from 6 counties in the high epidemic area along and north of the Huai River since March 2007. Then the villages of 6 counties were stratified into five levels according to the case reported in year 2006, and one village was randomly selected from each level, thereby 30 villages were selected in total.300-500 subjects were interviewed in each village, and in total 12 860 subjects were recruited in the study. The five selected villages in each county were allocated to three intervention groups according to the block randomization method. The first intervention group included 9 villages, 4362 people; the second intervention group was consisted of 12 villages, 4471 people; the non-intervention group had 9 villages, 4027 people. The basic information of the subjects were collected by questionnaire to analyze the relation between malaria cases and the distribution of the mosquito breeding water, then accordingly delimited the range for preventive medicine. Group 1 received the delimiting preventive medicine treatment, group 2 received routine medicine treatment, while non-treatment group received no treatment. The morbidity, standardized morbidity, net change of morbidity (the D-value of the standardized morbidity before and after the intervention), age-specified incidence, and the protective rate (PR), effectiveness index (IE) and the capture rate of the delimited method group were then calculated. RESULTS: Group 1 had 1219 (27.9%) people taking medicine and Group 2 had 219 (4.9%) people. In 2006, before the prevention conducting, the high incidence aging group in the first, second and nonintervention group was separately people aging 50 - 59, 60 - 69 and ≥ 70 years old; whose incidence was 36.22‰ (18/497), 40.11‰ (15/374) and 34.88‰ (9/258) respectively. After the intervention, the high incidence aging groups in the first and second intervention group changed to the population over 70 years old, with incidence at 9.17‰ (3/327) and 22.01‰ (7/318) respectively; while the high incidence aging groups in the nonintervention group changed to people aging between 30-39 years old, with the rate at 24.88‰ (10/402). In 2006, the morbidity of malaria in the first, second intervention group and nonintervention group was separately 18.78‰ (83/4420), 20.27‰ (93/4587) and 14.61‰ (53/3627); while the standardized incidence was separately 18.85‰, 20.72‰ and 14.89‰. In 2007, after the prevention conducting, the morbidity in the three groups was 2.75‰ (12/4362), 11.63‰ (52/4471) and 12.17‰ (49/4027), respectively; while the standardized incidences was 2.81‰, 12.75‰ and 12.35‰, respectively. The net value of changes of morbidity in the three groups was separately 16.04%, 7.97% and 2.54%. The difference in net values of changes of morbidity between intervention group 1 and 2 had statistical significance (χ(2) = 7.74, P < 0.05). Comparing with the nonintervention group, the PR and IE in intervention group 1 was separately 84.2% and 6.31; while the capture rate was 69.2% (9/13). CONCLUSION: The delimiting preventive medicine treatment during rest period of malaria was very effective for eliminating the potential infection source of malaria and reducing the morbidity of malaria.