The impacts of air pollution on circulatory and respiratory systems have been extensively studied. However, the associations between air pollution exposure and the risk of noncommunicable diseases of other organ systems, including diseases of the digestive, musculoskeletal, and genitourinary systems, remain unclear or inconclusive. We aimed to systematically assess the associations between short-term exposure to main air pollutants (fine particulate matter [PM_2.5] and ozone) and cause-specific risk of hospital admission in China over a wide spectrum of human diseases. Daily data on hospital admissions for primary diagnosis of 14 major and 188 minor disease categories in 252 Chinese cities (107 cities in North China and 145 cities in South China) from January 1, 2013, to December 31, 2017, were obtained from the Hospital Quality Monitoring System of China (covering 387 hospitals in North China and 614 hospitals in South China). We applied a 2-stage analytic approach to assess the associations between air pollution and daily hospital admissions. City-specific associations were estimated with quasi-Poisson regression models and then pooled by random-effects meta-analyses. Each disease category was analyzed separately, and the P values were adjusted for multiple comparisons. A total of 117,338,867 hospital admissions were recorded in the study period. Overall, 51.7% of the hospitalized cases were male, and 71.3% were aged <65 years. Robust positive associations were found between short-term PM_2.5 exposure and hospital admissions for 7 major disease categories: (1) endocrine, nutritional, and metabolic diseases; (2) nervous diseases; (3) circulatory diseases; (4) respiratory diseases; (5) digestive diseases; (6) musculoskeletal and connective tissue diseases; and (7) genitourinary diseases. For example, a 10-μg/m³ increase in PM_2.5 was associated with a 0.21% (95% CI 0.15% to 0.27%; adjusted P < 0.001) increase in hospital admissions for diseases of the digestive system on the same day in 2-pollutant models (adjusting for ozone). There were 35 minor disease categories significantly positively associated with same-day PM_2.5 in both single- and 2-pollutant models, including diabetes mellitus, anemia, intestinal infection, liver diseases, gastrointestinal hemorrhage, renal failure, urinary tract calculus, chronic ulcer of skin, and back problems. The association between short-term ozone exposure and respiratory diseases was robust. No safety threshold in the exposure–response relationships between PM_2.5 and hospital admissions was observed. The main limitations of the present study included the unavailability of data on personal air pollution exposures. In the Chinese population during 2013–2017, short-term exposure to air pollution, especially PM_2.5, was associated with increased risk of hospitalization for diseases of multiple organ systems, including certain diseases of the digestive, musculoskeletal, and genitourinary systems; many of these associations are important but still not fully recognized. The effect estimates and exposure–response relationships can inform policy making aimed at protecting public health from air pollution in China.

Antibiotic resistance poses a significant threat to human health, and wastewater treatment plants (WWTPs) are important reservoirs of antibiotic resistance genes (ARGs). Here, we analyze the antibiotic resistomes of 226 activated sludge samples from 142 WWTPs across six continents, using a consistent pipeline for sample collection, DNA sequencing and analysis. We find that ARGs are diverse and similarly abundant, with a core set of 20 ARGs present in all WWTPs. ARG composition differs across continents and is distinct from that of the human gut and the oceans. ARG composition strongly correlates with bacterial taxonomic composition, with Chloroflexi, Acidobacteria and Deltaproteobacteria being the major carriers. ARG abundance positively correlates with the presence of mobile genetic elements, and 57% of the 1112 recovered high-quality genomes possess putatively mobile ARGs. Resistome variations appear to be driven by a complex combination of stochastic processes and deterministic abiotic factors.

Single-cell mRNA sequencing, which permits whole transcriptional profiling of individual cells, has been widely applied to study growth and development of tissues and tumors. Resolving cell cycle for such groups of cells is significant, but may not be adequately achieved by commonly used approaches. Here we develop a traveling salesman problem and hidden Markov model-based computational method named reCAT, to recover cell cycle along time for unsynchronized single-cell transcriptome data. We independently test reCAT for accuracy and reliability using several data sets. We find that cell cycle genes cluster into two major waves of expression, which correspond to the two well-known checkpoints, G1 and G2. Moreover, we leverage reCAT to exhibit methylation variation along the recovered cell cycle. Thus, reCAT shows the potential to elucidate diverse profiles of cell cycle, as well as other cyclic or circadian processes (e.g., in liver), on single-cell resolution.