Ecological footprints and sustainable environmental management: A critical view of China's economy.

Global economies have recently been concerned about sustainable environmental management by reducing emissions and tackling ecological footprints. The rapid economic expansion and investment in traditional manufacturing further raises environmental degradation. China surpasses other emerging economies in the economic growth race yet has remained the top pollution-emitting economy for the last few decades, necessitating scholarly attention. This study examines the influencing factors of ecological footprints in China from the perspective of COP27. Using the extended dataset from 1988 to 2021, this study uses several time series diagnostic tests and verifies the existence of the long-run association between the study variables. Consequently, the non-linear scattered data leads to non-parametric (method of moment quantile regression) adoption. The empirical results indicate that only economic growth is a significant factor in environmental quality degradation in China. However, improving renewable energy usage, research and development, and foreign direct investment reduces the country's ecological footprint. Hence, the latter variables substantially lead to environmental sustainability. The robustness of the results is confirmed via a robust non-parametric estimator and causality test. Based on the empirical results, this study recommends increased investment in research and development, renewable production, and foreign direct investment enhancement.

How about the RCTs' quality of acupuncture treatment for female urinary incontinence in recent 20 years? A report quality assessment.

PURPOSE: To evaluate the quality of randomized controlled trials (RCTs) of acupuncture on female urinary incontinence (UI). METHODS: We searched for related RCTs of acupuncture on female UI from seven databases (PubMed, Embase, Cochrane library, Web of Science, Medline, ClinicalKey, and Clinical trials). We applied CONSORT (2010 year) and STRICTA criteria to evaluate the relevant factors of included RCTs. Two trained researchers scored independently, and concordance was assessed by Cohen's к-statistic. The median and interquartile range summarized the CONSORT and STRICTA scores of the included studies. In addition, two independent sample t tests were used to assess the differences in the study quality between the 2000-2010 and the 2011-2022 years. RESULTS: A total of 25 RCTs were finally included, and the scores were consistent between different assessors. The average CONSORT score was 10.50 (IQR 9.0-15.0) (total score was 25.0). Overall, the studies generally included scientific background (24/25, 96%), inclusion and exclusion criteria (24/25, 96%), outcome indicators (24/25, 96%), randomization methods (21/25, 84%), generalizability (19/25, 76%), and financial support (15/25, 60%). Most lacked sample size calculation (5/25, 20%), type of randomization (5/25, 20%), blinding (6/25, 24%), case screening period and follow-up (4/25, 16%), and study registration (6/25, 24%), etc. The average STRICTA score was 3.380 (IQR 3.02-3.95) (total score was 6.0). There had a lack of education on treatment methods for patients (3/25, 12%) and the qualification of acupuncturists (6/25, 24%). CONCLUSION: The overall quality of RCTs on acupuncture treatment of female UI was suboptimal. It is still necessary to improve the research methods, especially the application of random methods, blinding, the interpretation of treatment, and the identification qualification of acupuncturists.

Seasonal disparities and source tracking of airborne antibiotic resistance genes in Handan, China.

The transmission of airborne antibiotic resistance genes (ARGs) loaded on particle is a significant global public health concern. Up to date, the dispersal pattern of airborne ARGs remains unclear despite their critical role in multiregional transmission. In this study, airborne ARGs loaded on fine particulate matter (PM2.5) and source tracking based on the airflow trajectories were performed by the potential source contribution function (PSCF) and concentration weighted trajectory (CWT) model. The results show that the absolute abundance of ARG subtypes were generally twice times higher in the winter season than that in the summer season, which could be attributable to winter haze events with high particulate matter concentrations in Handan. Exogenous input from serious haze events and local release of ARGs loaded on PM2.5 of air masses may cause higher levels of ARGs in the winter. Moreover, based on the positive correlation between the abundance of ARGs and PM2.5 concentration, a source tracing model of airborne ARGs was proposed to the estimate of ARGs release and dissemination. This study highlights airborne ARGs transmission loaded on PM2.5 of air masses, which facilitating the global spread of antibiotic resistance.

Genomic Landscape and Phenotypic Assessment of Cronobacter sakazakii Isolated From Raw Material, Environment, and Production Facilities in Powdered Infant Formula Factories in China.

Cronobacter is a foodborne pathogen associated with severe infections and high mortality in neonates. The bacterium may also cause gastroenteritis, septicemia, and urinary tract and wound infectious in adults. A total of 15 Cronobacter isolates collected from 617 raw materials and environment samples from Powdered Infant Formula manufacturing factories during 2016 in Shaanxi, China, were analyzed for antimicrobial susceptibilities, species identification, biofilm formation, and whole-genome sequencing. The results showed that all 15 isolates were Cronobacter sakazakii, while the antimicrobial susceptibility test showed that all 15 C. sakazakii were pan susceptible. Most isolates were able to produce a weak biofilm, and two isolates from soil samples produced a strong biofilm formation. All isolates were classified into seven STs including ST4, ST40, ST64, ST93, ST148, ST256, and ST494, with ST64 (4/15, 26.7%) being dominant, and most were clinically related. The isolates harbored at least 11 virulence genes and two plasmids, with one isolate being positive for all virulence genes. Phylogenetic and ANI analysis showed strong clustering by sequence types and isolates from different sources or regions with a similar genomic background. The fact that isolates were obtained from raw materials and environment samples of PIF facilities shared a close phylogeny with one another suggests that cross-contamination events may have occurred between the processing room and external environments, which may give rise to a recurring risk of a continuous contamination during production.

Role of trade openness, export diversification, and renewable electricity output in realizing carbon neutrality dream of China.

Since climate change mitigation is the central debate of modern literature, the realization of carbon neutrality in response to diversified macroeconomic variables is the most crucial concern of international economies. However, the critical role of trade and renewable electricity output in export diversification-environmental nexus is missing. Therefore, this study investigates the combined influence of trade openness, exports diversification, and renewable electricity output on carbon dioxide emissions (CO2) in China from 1989 through 2019. Applying novel time series econometric techniques robust to structural breaks, following new outcomes are obtained. Firstly, long-run equilibrium cointegration existed among the under-analysis variables. Secondly, export diversification and renewable electricity output are predicted to decelerate CO2, supporting carbon neutrality in the long run. Thirdly, trade openness and gross domestic product accelerated the CO2, delaying carbon neutrality accomplishment. Most importantly, significant structural break dummy interacting with trade openness implicated that during the post-2001 era, China's trade openness extensively deteriorated the environmental quality in the face of trade liberalization obtained after joining the World Trade Organization (WTO). Based on empirical results, export diversification and renewable electricity production policies should be mutually non-exclusive and closely coordinated. Further, to counter the carbon emission acceleration impact of trade openness, increasing the share of green tradable products is suggested. Finally, bilateral trade restructuring is recommended to realize the long-term dream of economic sustainability and carbon neutrality.

A latent class approach for joint modeling of a time-to-event outcome and multiple longitudinal biomarkers subject to limits of detection.

Multiple biomarkers on different biological pathways are often measured over time to investigate the complex mechanism of disease development and progression. Identification of informative subpopulation patterns of longitudinal biomarkers and clinical endpoint may assist in risk stratification and provide insights into new therapeutic targets. Motivated by a multicenter study to assess the inflammatory markers of sepsis in patients with community-acquired pneumonia, we propose a joint latent class analysis of multiple biomarkers and a time-to-event outcome while accounting for censored biomarker measurements due to detection limits. The interrelationship between biomarker trajectories and clinical endpoint is fully captured by a latent class structure, which reveals the subpopulation profiles of biomarkers and clinical outcome. The estimation of joint latent class models becomes more complicated when biomarkers are subject to detection limits. Based on a Metropolis-Hastings method, we develop a Monte Carlo Expectation-Maximization (MCEM) algorithm to estimate model parameters. We demonstrate the satisfactory performance of our MCEM algorithm using simulation studies, and apply our method to the motivating study to examine the heterogeneous patterns of cytokine responses to pneumonia and associated mortality risks.

Joint analysis of left-censored longitudinal biomarker and binary outcome via latent class modeling.

Joint latent class modeling is an appealing approach for evaluating the association between a longitudinal biomarker and clinical outcome when the study population is heterogeneous. The link between the biomarker trajectory and the risk of event is reflected by the latent classes, which accommodate the underlying population heterogeneity. The estimation of joint latent class models may be complicated by the censored data in the biomarker measurements due to detection limits. We propose a modified likelihood function under the parametric assumption of biomarker distribution and develop a Monte Carlo expectation-maximization algorithm for joint analysis of a biomarker and a binary outcome. We conduct simulation studies to demonstrate the satisfactory performance of our Monte Carlo expectation-maximization algorithm and the superiority of our method to the naive imputation method for handling censored biomarker data. In addition, we apply our method to the Genetic and Inflammatory Markers of Sepsis study to investigate the role of inflammatory biomarker profile in predicting 90-day mortality for patients hospitalized with community-acquired pneumonia.