Deciphering the impact of urban built environment density on respiratory health using a quasi-cohort analysis of 5495 non-smoking lung cancer cases.

INTRODUCTION: Lung cancer is a major health concern and is influenced by air pollution, which can be affected by the density of urban built environment. The spatiotemporal impact of urban density on lung cancer incidence remains unclear, especially at the sub-city level. We aimed to determine cumulative effect of community-level density attributes of the built environment on lung cancer incidence in high-density urban areas. METHODS: We selected 78 communities in the central city of Shanghai, China as the study site; communities included in the analysis had an averaged population density of 313 residents per hectare. Using data from the city cancer surveillance system, an age-period-cohort analysis of lung cancer incidence was performed over a five-year period (2009-2013), with a total of 5495 non-smoking/non-secondhand smoking exposure lung cancer cases. Community-level density measures included the density of road network, facilities, buildings, green spaces, and land use mixture. RESULTS: In multivariate models, built environment density and the exposure time duration had an interactive effect on lung cancer incidence. Lung cancer incidence of birth cohorts was associated with road density and building coverage across communities, with a relative risk of 1·142 (95 % CI: 1·056-1·234, P = 0·001) and 1·090 (95 % CI: 1·053-1·128, P < 0·001) at the baseline year (2009), respectively. The relative risk increased exponentially with the exposure time duration. As for the change in lung cancer incidence over the five-year period, lung cancer incidence of birth cohorts tended to increase faster in communities with a higher road density and building coverage. CONCLUSION: Urban planning policies that improve road network design and building layout could be important strategies to reduce lung cancer incidence in high-density urban areas.

Erratum: Corrigendum: The distribution and behavioral characteristics of plateau pikas (Ochotonacurzoniae). ZooKeys 1059: 157-171. https://doi.org/10.3897/zookeys.1059.63581.

[This corrects the article DOI: 10.3897/zookeys.1059.63581.].

Neoadjuvant chemotherapy with liposomal paclitaxel plus platinum for locally advanced esophageal squamous cell cancer: Results from a retrospective study.

BACKGROUND: This study analyzed the efficacy and safety of neoadjuvant chemotherapy with liposomal paclitaxel plus platinum in patients with locally advanced resectable esophageal squamous cell carcinoma (ESCC). METHODS: The data of patients with locally advanced resectable ESCC (staging cT2N + M0, cT3-4aNanyM0, IA-IVA) who received preoperative chemotherapy with liposomal paclitaxel plus platinum (cisplatin, nedaplatin or carboplatin) in HuanXing Cancer Hospital from July 2018 to October 2019 were collected. The primary endpoint of this study was R0 resection rate, and secondary endpoints were pathological complete response (pCR) rate, 1- and 2-year overall survival (OS) rate, 1-year and 18-month disease-free survival (DFS) rate, and safety. RESULTS: A total of 32 eligible patients were included in this study. All patients received neoadjuvant chemotherapy and surgery. The R0 resection rate was 93.8%, the pCR rate was 12.5%, and down-staging was achieved in 14 patients (47.8%). Median follow-up was 31.0 months (95% confidence interval [CI] 30.1-31.9 months). The 1- and 2-year OS rates were 96.9% and 78.1%, and the 1-year and 18-month DFS rates were 86.7% and 76.7%, respectively. The median DFS and OS were not reached. The incidence rate of neoadjuvant chemotherapy related grade 3-4 adverse events was 21.9%, including neutropenia (21.9%) and leukopenia (9.4%). CONCLUSIONS: The results of this study suggest that liposomal paclitaxel combined with platinum as neoadjuvant chemotherapy can provide satisfactory R0 resection rate and survival rate, and significant tumor down-staging effect for patients with locally advanced resectable ESCC, with safety profile.

The distribution and behavioral characteristics of plateau pikas (Ochotonacurzoniae).

Plateau pikas (Ochotonacurzoniae) are regarded as one of the main causes of the degradation of alpine meadows in the Qinghai-Tibet Plateau (QTP). The population density of plateau pikas is directly related to the degree of grassland damage. In this study, field observation was conducted for one week in the southeastern QTP in August 2019. A random encounter model (REM) was used to estimate the population density of plateau pikas from photographs and videos, and the frequencies of different behaviors were calculated. In addition, the effects of water-source distance and terrain on the distribution of plateau pikas and the frequencies of different pika behaviors under different population densities were explored. The observations and knowledge derived from this study provide a reference for the population control of plateau pikas.

A density estimation model of plateau pika (Ochotona curzoniae) supporting camera-monitoring programs.

As an important species in the Qinghai-Tibet Plateau, the roles played by plateau pikas in grassland degradation and protection are controversial. The behavior characteristics and population density of this species are important in answering this question, but these traits have not been fully elucidated to date. Camera-capture methods have been used widely in recent years to characterize or calculate population density with the advantage of simple operation and nonintrusive investigation. However, establishing the relationship between actual population density and monitoring data with the condition that individual identification is not possible is a major challenge for this method. In this study, a model composed of a behavioral module and a burrow system module is proposed and applied to simulate the moving path of each individual pika. Based on Monte Carlo method, the model is used to develop the relationship between population density and recorded capture number, which is compared with the results derived from the random encounter model (REM) based on field observations. The simulated results mixed with the calculated density based on observation data could reach R 2 = 0.98 using linear fitting, with proper parameter settings. A novel index named activity intensity of pikas per population density is also proposed, providing information on both the ecological physical characteristics and monitoring space. The influence of different parameters on this index, mainly the pika number per burrow system, pika activity time outside the burrow, and activity intensity, is discussed. The proposed methodology can be applied to different scenarios in further studies when behavioral characteristics of pikas change for such reasons as climate change and vegetation degradation.

A Novel Crow Swarm Optimization Algorithm (CSO) Coupling Particle Swarm Optimization (PSO) and Crow Search Algorithm (CSA).

The balance between exploitation and exploration essentially determines the performance of a population-based optimization algorithm, which is also a big challenge in algorithm design. Particle swarm optimization (PSO) has strong ability in exploitation, but is relatively weak in exploration, while crow search algorithm (CSA) is characterized by simplicity and more randomness. This study proposes a new crow swarm optimization algorithm coupling PSO and CSA, which provides the individuals the possibility of exploring the unknown regions under the guidance of another random individual. The proposed CSO algorithm is tested on several benchmark functions, including both unimodal and multimodal problems with different variable dimensions. The performance of the proposed CSO is evaluated by the optimization efficiency, the global search ability, and the robustness to parameter settings, all of which are improved to a great extent compared with either PSO and CSA, as the proposed CSO combines the advantages of PSO in exploitation and that of CSA in exploration, especially for complex high-dimensional problems.

A potential target for liver cancer management, lysophosphatidic acid receptor 6 (LPAR6), is transcriptionally up-regulated by the NCOA3 coactivator.

Lysophosphatidic acid receptor 6 (LPAR6) is a G protein-coupled receptor that plays critical roles in cellular morphology and hair growth. Although LPAR6 overexpression is also critical for cancer cell proliferation, its role in liver cancer tumorigenesis and the underlying mechanism are poorly understood. Here, using liver cancer and matched paracancerous tissues, as well as functional assays including cell proliferation, quantitative real-time PCR, RNA-Seq, and ChIP assays, we report that LPAR6 expression is controlled by a mechanism whereby hepatocyte growth factor (HGF) suppresses liver cancer growth. We show that high LPAR6 expression promotes cell proliferation in liver cancer. More importantly, we find that LPAR6 is transcriptionally down-regulated by HGF treatment and that its transcriptional suppression depends on nuclear receptor coactivator 3 (NCOA3). We note that enrichment of NCOA3, which has histone acetyltransferase activity, is associated with histone 3 Lys-27 acetylation (H3K27ac) at the LPAR6 locus in response to HGF treatment, indicating that NCOA3 transcriptionally regulates LPAR6 through the HGF signaling cascade. Moreover, depletion of either LPAR6 or NCOA3 significantly inhibited tumor cell growth in vitro and in vivo (in mouse tumor xenograft assays), similar to the effect of the HGF treatment. Collectively, our findings indicate an epigenetic link between LPAR6 and HGF signaling in liver cancer cells, and suggest that LPAR6 can serve as a biomarker and new strategy for therapeutic interventions for managing liver cancer.