Subgroup analysis for longitudinal data based on a partial linear varying coefficient model with a change plane.

Subgroup analysis has become an important tool to characterize the treatment effect heterogeneity, and finally towards precision medicine. On the other hand, longitudinal study is widespread in many fields, but subgroup analysis for this data type is still limited. In this article, we study a partial linear varying coefficient model with a change plane, in which the subgroups are defined based on linear combination of grouping variables, and the time-varying effects in different subgroups are estimated to capture the dynamic association between predictors and response. The varying coefficients are approximated by basis functions and the group indicator function is smoothed by kernel function, which are included in the generalized estimating equation for estimation. Asymptotic properties of the estimators for the varying coefficients, the constant coefficients and the change plane coefficients are established. Simulations are conducted to demonstrate the flexibility, efficiency and robustness of the proposed method. Based on the Standard and New Antiepileptic Drugs study, we successfully identify a subgroup in which patients are sensitive to the newer drug in a specific period of time.

Multiply robust subgroup analysis based on a single-index threshold linear marginal model for longitudinal data with dropouts.

Identifying subpopulations that may be sensitive to the specific treatment is an important step toward precision medicine. On the other hand, longitudinal data with dropouts is common in medical research, and subgroup analysis for this data type is still limited. In this paper, we consider a single-index threshold linear marginal model, which can be used simultaneously to identify subgroups with differential treatment effects based on linear combination of the selected biomarkers, estimate the treatment effects in different subgroups based on regression coefficients, and test the significance of the difference in treatment effects based on treatment-subgroup interaction. The regression parameters are estimated by solving a penalized smoothed generalized estimating equation and the selection bias caused by missingness is corrected by a multiply robust weighting matrix, which allows multiple missingness models to be taken account into estimation. The proposed estimator remains consistent when any model for missingness is correctly specified. Under regularity conditions, the asymptotic normality of the estimator is established. Simulation studies confirm the desirable finite-sample performance of the proposed method. As an application, we analyze the data from a clinical trial on pancreatic cancer.

Exosomes derived from human umbilical cord mesenchymal stem cells accelerate growth of VK2 vaginal epithelial cells through MicroRNAs in vitro.

STUDY QUESTION: Could human umbilical cord mesenchymal stem cell-derived exosomes (hucMSC-Ex) accelerate vaginal epithelium cell (VK2) growth? SUMMARY ANSWER: HucMSC-Ex play a significant role in promoting proliferation of VK2 cells by accelerating the cell cycle and inhibiting apoptosis through exosomal microRNAs in vitro. WHAT IS KNOWN ALREADY: Numerous studies have reported that MSC-Ex play an important role in tissue injury repair. STUDY DESIGN, SIZE, DURATION: hucMSC and exosomes isolated from their conditioned medium were used to treat a vaginal epithelial cell line (VK2). Normal human fibroblasts (HFF-1) were used as negative control to hucMSC. PARTICIPANTS/MATERIALS, SETTING, METHODS: VK2 cells were co-cultured with hucMSC whose paracrine effect on the viability, cell cycle and cell apoptosis of VK2 vaginal epithelial cells was further assessed by the CCK-8 assay and flow cytometry. HucMSC-Ex isolated from culture medium by ultracentrifuge were characterized by transmission electron microscopy, nanoparticle tracking analysis and Western blot. HucMSC-Ex at different concentrations and HFF-1 exosomes were used to treat VK2 cells. High-throughput RNA sequencing was utilized to reveal the profile of microRNAs in hucMSC, hucMSC-Ex, HFF-1 and HFF-1 exosomes and GO analysis was applied to demonstrate their functions. To evaluate the function of these specific microRNAs in hucMSC-Ex, VK2 cells were treated with RNA-interfered-hucMSC-Ex (RNAi-hucMSC-Ex) and their proliferation was measured by Label-free Real-time Cellular Analysis System. MAIN RESULTS AND THE ROLE OF CHANCE: The study showed that hucMSC stimulate VK2 cell growth possibly through a paracrine route by promoting cell cycle and inhibiting apoptosis. Compared with control and low dose groups, hucMSC-Ex of high concentration (more than 1000 ng/ml) significantly increased VK2's growth after treatment in a dose-depended manner (P < 0.05). HucMSC-Ex raised the proportion of cells in S-phase and reduced the percentage of apoptotic cells in VK2 cells in comparison with the HFF-1 exosomes and control groups (P < 0.05). microRNAs, including miR-100 (16.92%), miR-146a (9.21%), miR-21 (6.67%), miR-221 (6.39%) and miR-143 (4.63%), were found to be specifically enriched (P < 0.05) in hucMSC-Ex and their functions concentrated on cell cycle, development and differentiation. Collectively, our findings indicate that hucMSC-Ex may play a significant role in accelerating VK2's proliferation by promoting cell cycle and inhibiting apoptosis through exosomal microRNAs in vitro. LARGE-SCALE DATA: N/A. LIMITATIONS, REASONS FOR CAUTION: Our study did not confirm the function of hucMSC-Ex or specifically enriched exosomal microRNAs in vivo. miR-100 and miR-146a are well-known immunomodulatory miRNAs that participate in the regulation of inflammatory disorders and may enhance the therapeutic effect of hucMSC-Ex by promoting the surgical injury repair after vaginal reconstruction. But whether it acts through anti-inflammatory responses needs further study. WIDER IMPLICATIONS OF THE FINDINGS: This finding supports the potential use of hucMSC-Ex as a cell-free therapy of Meyer-Rokitansky-Küster-Hauser syndrome (MRKHS) after vaginoplasty. STUDY FUNDING/COMPETING INTEREST(S): This study was supported by the Chinese National Nature Sciences Foundation (grant number 91440107, 81471416 and 81771524) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB19040102). All authors state that there is no conflict of interest to disclose.

Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout, with applications to quality of life measurements from clinical trials.

The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.

Progress and challenges of sequencing and analyzing circulating tumor cells.

Circulating tumor cells (CTCs) slough off primary tumor tissues and are swept away by the circulatory system. These CTCs can remain in circulation or colonize new sites, forming metastatic clones in distant organs. Recently, CTC analyses have been successfully used as effective clinical tools to monitor tumor progression and prognosis. With advances in next-generation sequencing (NGS) and single-cell sequencing (SCS) technologies, scientists can obtain the complete genome of a CTC and compare it with corresponding primary and metastatic tumors. CTC sequencing has been successfully applied to monitor genomic variations in metastatic and recurrent tumors, infer tumor evolution during treatment, and examine gene expression as well as the mechanism of the epithelial-mesenchymal transition. However, compared with cancer biopsy sequencing and circulating tumor DNA sequencing, the sequencing of CTC genomes and transcriptomes is more complex and technically difficult. Challenges include enriching pure tumor cells from a background of white blood cells, isolating and collecting cells without damaging or losing DNA and RNA, obtaining unbiased and even whole-genome and transcriptome amplification material, and accurately analyzing CTC sequencing data. Here, we review and summarize recent studies using NGS on CTCs. We mainly focus on CTC genome and transcriptome sequencing and the biological and potential clinical applications of these methodologies. Finally, we discuss challenges and future perspectives of CTC sequencing.

Doubly robust estimation of generalized partial linear models for longitudinal data with dropouts.

We develop a doubly robust estimation of generalized partial linear models for longitudinal data with dropouts. Our method extends the highly efficient aggregate unbiased estimating function approach proposed in Qu et al. (2010) to a doubly robust one in the sense that under missing at random (MAR), our estimator is consistent when either the linear conditional mean condition is satisfied or a model for the dropout process is correctly specified. We begin with a generalized linear model for the marginal mean, and then move forward to a generalized partial linear model, allowing for nonparametric covariate effect by using the regression spline smoothing approximation. We establish the asymptotic theory for the proposed method and use simulation studies to compare its finite sample performance with that of Qu's method, the complete-case generalized estimating equation (GEE) and the inverse-probability weighted GEE. The proposed method is finally illustrated using data from a longitudinal cohort study.

Robust estimation of partially linear models for longitudinal data with dropouts and measurement error.

Outliers, measurement error, and missing data are commonly seen in longitudinal data because of its data collection process. However, no method can address all three of these issues simultaneously. This paper focuses on the robust estimation of partially linear models for longitudinal data with dropouts and measurement error. A new robust estimating equation, simultaneously tackling outliers, measurement error, and missingness, is proposed. The asymptotic properties of the proposed estimator are established under some regularity conditions. The proposed method is easy to implement in practice by utilizing the existing standard generalized estimating equations algorithms. The comprehensive simulation studies show the strength of the proposed method in dealing with longitudinal data with all three features. Finally, the proposed method is applied to data from the Lifestyle Education for Activity and Nutrition study and confirms the effectiveness of the intervention in producing weight loss at month 9. Copyright © 2016 John Wiley & Sons, Ltd.

Risk factor selection in rate making: EM adaptive LASSO for zero-inflated poisson regression models.

Risk factor selection is very important in the insurance industry, which helps precise rate making and studying the features of high-quality insureds. Zero-inflated data are common in insurance, such as the claim frequency data, and zero-inflation makes the selection of risk factors quite difficult. In this article, we propose a new risk factor selection approach, EM adaptive LASSO, for a zero-inflated Poisson regression model, which combines the EM algorithm and adaptive LASSO penalty. Under some regularity conditions, we show that, with probability approaching 1, important factors are selected and the redundant factors are excluded. We investigate the finite sample performance of the proposed method through a simulation study and the analysis of car insurance data from SAS Enterprise Miner database.

Urine metabolites and viral pneumonia among children: a case-control study in China.

Background: Viral pneumonia in children is common and has grave consequences. The study aims to better understand the pathophysiological processes involved in the onset and progression of viral pneumonia and identify common effects or biomarkers across different viruses. Methods: This study collected urine samples from 96 patients with viral pneumonia including respiratory syncytial virus (RSV) (n=30), influenza virus (IV) (n=23), parainfluenza virus (PIV) (n=24), and adenovirus (ADV) (n=19), and 31 age- and sex-matched normal control (NC) subjects. The samples were analyzed using liquid chromatography coupled with mass spectrometry (LC-MS) to identify endogenous substances. The XCMS Online platform was utilized for data processing and analysis , including feature detection, retention time correction, alignment, annotation, and statistical analysis for difference between groups and biomarker identification. Results: A total of 948 typical metabolites were identified using the XCMS Online platform with the Mummichog technique. After analyzing the data, 24 metabolites were selected as potential biomarkers for viral pneumonia, of which 16 were aspartate and asparagine metabolites, byproducts of alanine, leucine, and isoleucine degradation, and butanoate metabolites. Conclusions: This study specific metabolites and altered pathways in children with viral pneumonia and propose that these findings could contribute to the discovery of new treatments and the development of antiviral drugs.

Impact of fireworks burning on air quality during the Spring Festival in 2021-2022 in Linyi, a central city in the North China Plain.

The management of fireworks has been strengthened during the Spring Festival in 2022 compared with that in 2021 in Linyi, a central city in the North China Plain. Online measurements of the chemical components of PM2.5 were conducted during the Spring Festival in 2021-2022 to assess the influence of fireworks burning (FB) on air quality. Remarkable achievements have been made in improving air quality during FB period (FBP) in 2021-2022 attributing to the stringent regional emission reduction measures, fireworks control, and favorable meteorological conditions with the concentrations of PM2.5, water-soluble ions, and carbonaceous aerosols decreasing by 73.6%, 78.8%, and 73.5%, respectively. The PM2.5 concentrations increased by 96.3% during FBP compared with those during non-FB period (NFBP) in 2021, while the opposite phenomenon was observed in 2022 with PM2.5 concentrations decreasing by 56.2% because of the favorable meteorological condition during FBP in 2022. As indicators of FB, the Cl-, K+, and Mg2+ concentrations showed an increasing trend during FBP compared with that during NFBP, both in 2021 and 2022, but had little effect on other components. The contribution of FB to PM2.5 decreased from 68.4% in 2021 to 15.7% in 2022 based on the relative ratio method, indicating the various measures conducted by all districts and counties in Linyi helped achieve near zero fireworks emissions by 2022. Besides, the contribution of FB to PM2.5 showed a straight-line upward trend from 19:00 on New Year's Eve, reached its peak (76.1%) at 3:00 on Lunar New Year's Day, while the highest value was only 35.0% during FBP in 2022, indicating the implementation of fireworks ban measures in Linyi achieved a good effect on pollution peak cutting. This study has emphasized the necessity of FB restricting during special holidays.

Integrating host transcriptomic signatures for distinguishing autoimmune encephalitis in cerebrospinal fluid by metagenomic sequencing.

BACKGROUND: The early accurate diagnoses for autoimmune encephalitis (AE) and infectious encephalitis (IE) are essential since the treatments for them are different. This study aims to discover some specific and sensitive biomarkers to distinguish AE from IE at early stage to give specific treatments for good outcomes. RESULTS: We compared the host gene expression profiles and microbial diversities of cerebrospinal fluid (CSF) from 41 patients with IE and 18 patients with AE through meta-transcriptomic sequencing. Significant differences were found in host gene expression profiles and microbial diversities in CSF between patients with AE and patients with IE. The most significantly upregulated genes in patients with IE were enriched in pathways related with immune response such as neutrophil degranulation, antigen processing and presentation and adaptive immune system. In contrast, those upregulated genes in patients with AE were mainly involved in sensory organ development such as olfactory transduction, as well as synaptic transmission and signaling. Based on the differentially expressed genes, a classifier consisting of 5 host genes showed outstanding performance with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95. CONCLUSIONS: This study provides a promising classifier and is the first to investigate transcriptomic signatures for differentiating AE from IE by using meta-transcriptomic next-generation sequencing technology.

Metatranscriptomic analysis revealed Prevotella as a potential biomarker of oropharyngeal microbiomes in SARS-CoV-2 infection.

Background and objectives: Disease severity and prognosis of coronavirus disease 2019 (COVID-19) disease with other viral infections can be affected by the oropharyngeal microbiome. However, limited research had been carried out to uncover how these diseases are differentially affected by the oropharyngeal microbiome of the patient. Here, we aimed to explore the characteristics of the oropharyngeal microbiota of COVID-19 patients and compare them with those of patients with similar symptoms. Methods: COVID-19 was diagnosed in patients through the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by quantitative reverse transcription polymerase chain reaction (RT-qPCR). Characterization of the oropharyngeal microbiome was performed by metatranscriptomic sequencing analyses of oropharyngeal swab specimens from 144 COVID-19 patients, 100 patients infected with other viruses, and 40 healthy volunteers. Results: The oropharyngeal microbiome diversity in patients with SARS-CoV-2 infection was different from that of patients with other infections. Prevotella and Aspergillus could play a role in the differentiation between patients with SARS-CoV-2 infection and patients with other infections. Prevotella could also influence the prognosis of COVID-19 through a mechanism that potentially involved the sphingolipid metabolism regulation pathway. Conclusion: The oropharyngeal microbiome characterization was different between SARS-CoV-2 infection and infections caused by other viruses. Prevotella could act as a biomarker for COVID-19 diagnosis and of host immune response evaluation in SARS-CoV-2 infection. In addition, the cross-talk among Prevotella, SARS-CoV-2, and sphingolipid metabolism pathways could provide a basis for the precise diagnosis, prevention, control, and treatment of COVID-19.

A multicenter prospective study of comprehensive metagenomic and transcriptomic signatures for predicting outcomes of patients with severe community-acquired pneumonia.

BACKGROUND: Severe community-acquired pneumonia (SCAP) results in high mortality as well as massive economic burden worldwide, yet limited knowledge of the bio-signatures related to prognosis has hindered the improvement of clinical outcomes. Pathogen, microbes and host are three vital elements in inflammations and infections. This study aims to discover the specific and sensitive biomarkers to predict outcomes of SCAP patients. METHODS: In this study, we applied a combined metagenomic and transcriptomic screening approach to clinical specimens gathered from 275 SCAP patients of a multicentre, prospective study. FINDINGS: We found that 30-day mortality might be independent of pathogen category or microbial diversity, while significant difference in host gene expression pattern presented between 30-day mortality group and the survival group. Twelve outcome-related clinical characteristics were identified in our study. The underlying host response was evaluated and enrichment of genes related to cell activation, immune modulation, inflammatory and metabolism were identified. Notably, omics data, clinical features and parameters were integrated to develop a model with six signatures for predicting 30-day mortality, showing an AUC of 0.953 (95% CI: 0.92-0.98). INTERPRETATION: In summary, our study linked clinical characteristics and underlying multi-omics bio-signatures to the differential outcomes of patients with SCAP. The establishment of a comprehensive predictive model will be helpful for future improvement of treatment strategies and prognosis with SCAP. FUNDING: National Natural Science Foundation of China (No. 82161138018), Shanghai Municipal Key Clinical Specialty (shslczdzk02202), Shanghai Top-Priority Clinical Key Disciplines Construction Project (2017ZZ02014), Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases (20dz2261100).

Robust estimation of models for longitudinal data with dropouts and outliers.

Missing data and outliers usually arise in longitudinal studies. Ignoring the effects of missing data and outliers will make the classical generalized estimating equation approach invalid. The longitudinal cohort study of rheumatoid arthritis patients was designed to investigate whether the Health Assessment Questionnaire score was associated with baseline covariates and changed with time. There exist dropouts and outliers in the data. In order to analyze the data, we develop a robust estimating equation approach. To deal with the responses missing at random, we extend a doubly robust method. To achieve robustness against outliers, we utilize an outlier robust method, which corrects the bias induced by outliers through centralizing the covariate matrix in the estimating equation. The doubly robust method for dropouts is easy to combine with the outlier robust method. The proposed method has the property of robustness in the sense that the proposed estimator is not only doubly robust against model misspecification for dropouts when there is no outlier in the data, but also robust against outliers. Consistency and asymptotic normality of the proposed estimator are established under regularity conditions. A comprehensive simulation study and real data analysis demonstrate that the proposed estimator does have the property of robustness.

Exosome Mediated Cytosolic Cisplatin Delivery Through Clathrin-Independent Endocytosis and Enhanced Anti-cancer Effect via Avoiding Endosome Trapping in Cisplatin-Resistant Ovarian Cancer.

Background: Ovarian carcinoma is one of the most common gynecologic malignancies, cisplatin resistance has become a key obstacle to the successful treatment of ovarian cancer because ovarian carcinomas are liable to drug resistance. To find an effective drug carrier is an urgent need. Methods: Exosomes and loading-cisplatin exosomes are isolated using differential centrifugation and characterized by transmission, electron, nanoparticle tracking analysis. The anti-cancer effect of cisplatin was detected under the circumstance of delivered by exosomes or without exosomes in vitro and in vivo. Using proteome analysis and bioinformatics analysis, we further discovered the pathways in exosomes delivery process. We employed a con-focal immunofluorescence analysis, to evaluate the effects of milk-exosomes deliver the cisplatin via avoiding endosomal trapping. Results: Exosomes and exosome-cisplatin were characterized including size, typical markers including CD63, Alix and Tsg101. The anti-cancer effect of cisplatin was enhanced when delivered by exosome in vitro and in vivo. Mechanistic studies shown that exosomes deliver cisplatin mostly via clathrin-independent endocytosis pathway. Exosomes deliver cisplatin into cisplatin-resistant cancer cells clathrin-independent endocytosis and enhance the anti-cancer effect through avoiding endosome trapping. Conclusion: Cisplatin could be delivered by exosome through clathrin-independent endocytosis, and could evade the endosome trapping, diffused in the cytosol evenly. Our study clarifies the mechanism of exosomes mediated drug delivery against resistant cancer, indicates that exosomes can be a potential nano-carrier for cisplatin against cisplatin resistant ovarian cancer, which validates and enriches the theory of intracellular exosome trafficking.

Ozone pollution in the plate and logistics capital of China: Insight into the formation, source apportionment, and regional transport.

As the logistics and plate capital of China, the sources and regional transport of O3 in Linyi are different from those in other cities because of the significant differences in industrial structure and geographical location. Twenty-five ozone pollution episodes (OPEs, 52 days) were identified in 2021, with a daily maximum 8-h moving average O3 concentration (O3-MDA8) of 184.5 ± 22.5 µg/m3. Oxygenated volatile organic compounds (OVOCs) and aromatics were the dominant contributors to ozone formation potential (OFP), with contributions of approximately 23.5-52.7% and 20.0-40.8%, respectively, followed by alkenes, alkanes, and alkynes. Formaldehyde, an OVOC with high concentrations emitted from the plate industry and vehicles, contributed the most to OFP (22.7 ± 5.5%), although formaldehyde concentrations only accounted for 9.4 ± 2.7% of the total non-methane hydrocarbon (NMHC) concentrations. The source apportionment results indicated that the plate industry was the dominant O3 contributor (27.0%), followed by other sources (21.6%), vehicle-related sources (18.0%), solvent use (16.9%), liquefied petroleum gas (LPG)/natural gas (NG) (8.8%), and combustion sources (7.7%). Therefore, there is an urgent need to control the plating industry in Linyi to mitigate O3 pollution. The backward trajectory, potential source contribution function (PSCF), and concentration weighted trajectory (CWT) models were used to identify the air mass pathways and potential source areas of air pollutants during the OPEs. O3 pollution was predominantly affected by air masses that originated from eastern and local regions, while trajectories from the south contained the highest O3 concentrations (207.0 µg/m3). The potential source area was from east and south Linyi during the OPEs. Therefore, it is critical to implement regional joint prevention and control measures to lower O3 concentrations.

Metatranscriptomic Analysis Reveals Disordered Alterations in Oropharyngeal Microbiome during the Infection and Clearance Processes of SARS-CoV-2: A Warning for Secondary Infections.

This study was conducted to investigate oropharyngeal microbiota alterations during the progression of coronavirus disease 2019 (COVID-19) by analyzing these alterations during the infection and clearance processes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The diagnosis of COVID-19 was confirmed by using positive SARS-CoV-2 quantitative reverse transcription polymerase chain reaction (RT-qPCR). The alterations in abundance, diversity, and potential function of the oropharyngeal microbiome were identified using metatranscriptomic sequencing analyses of oropharyngeal swab specimens from 47 patients with COVID-19 (within a week after diagnosis and within two months after recovery from COVID-19) and 40 healthy individuals. As a result, in the infection process of SARS-CoV-2, compared to the healthy individuals, the relative abundances of Prevotella, Aspergillus, and Epstein-Barr virus were elevated; the alpha diversity was decreased; the beta diversity was disordered; the relative abundance of Gram-negative bacteria was increased; and the relative abundance of Gram-positive bacteria was decreased. After the clearance of SARS-CoV-2, compared to the healthy individuals and patients with COVID-19, the above disordered alterations persisted in the patients who had recovered from COVID-19 and did not return to the normal level observed in the healthy individuals. Additionally, the expressions of several antibiotic resistance genes (especially multi-drug resistance, glycopeptide, and tetracycline) in the patients with COVID-19 were higher than those in the healthy individuals. After SARS-CoV-2 was cleared, the expressions of these genes in the patients who had recovered from COVID-19 were lower than those in the patients with COVID-19, and they were different from those in the healthy individuals. In conclusion, our findings provide evidence that potential secondary infections with oropharyngeal bacteria, fungi, and viruses in patients who have recovered from COVID-19 should not be ignored; this evidence also highlights the clinical significance of the oropharyngeal microbiome in the early prevention of potential secondary infections of COVID-19 and suggests that it is imperative to choose appropriate antibiotics for subsequent bacterial secondary infection in patients with COVID-19.

Microbial and human transcriptional profiling of coronavirus disease 2019 patients: Potential predictors of disease severity.

The high morbidity of patients with coronavirus disease 2019 (COVID-19) brings on a panic around the world. COVID-19 is associated with sex bias, immune system, and preexisting chronic diseases. We analyzed the gene expression in patients with COVID-19 and in their microbiota in order to identify potential biomarkers to aid in disease management. A total of 129 RNA samples from nasopharyngeal, oropharyngeal, and anal swabs were collected and sequenced in a high-throughput manner. Several microbial strains differed in abundance between patients with mild or severe COVID-19. Microbial genera were more abundant in oropharyngeal swabs than in nasopharyngeal or anal swabs. Oropharyngeal swabs allowed more sensitive detection of the causative SARS-CoV-2. Microbial and human transcriptomes in swabs from patients with mild disease showed enrichment of genes involved in amino acid metabolism, or protein modification via small protein removal, and antibacterial defense responses, respectively, whereas swabs from patients with severe disease showed enrichment of genes involved in drug metabolism, or negative regulation of apoptosis execution, spermatogenesis, and immune system, respectively. Microbial abundance and diversity did not differ significantly between males and females. The expression of several host genes on the X chromosome correlated negatively with disease severity. In this way, our analyses identify host genes whose differential expression could aid in the diagnosis of COVID-19 and prediction of its severity via non-invasive assay.

Variations in characteristics and transport pathways of PM2.5 during heavy pollution episodes in 2013-2019 in Jinan, a central city in the north China Plain.

The characteristics and transport pathways of air masses vary during heavy pollution episodes (HPEs). Three categories of HPEs have been defined: HPE Ι, II, and III, corresponding to HPE durations of 1, 2, and at least 3 days, respectively. Sixty HPEs were investigated in this study. The number of HPEs decreased from 2013 to 2017 and then increased from 2017 to 2019, dominated by emission reductions and meteorological conditions. The average and maximum PM2.5 (i.e., aerodynamic diameter of <2.5 µm) concentrations during those HPEs in 2019 decreased by 5.6%-11.8% and 11.9%-38.5%, respectively, compared with those in 2013. The longer the duration of an HPE, the higher the PM2.5 concentration. Secondary inorganic aerosol concentrations and their contents in PM2.5 during HPE Ⅲ were found to be higher than those during HPEs Ι and Ⅱ, as secondary transformations of precursor gases are more intense during long-term HPEs. The dominant trajectories of airflow arriving in Jinan originated from the southern and southeastern regions during HPEs, realized using the Hybrid Single Particle Lagrangian Integrated Trajectory. The trajectories from the north and west of Jinan contained the highest PM2.5 concentrations of 323.3-432.1 µg/m3 during HPE Ⅲ, although these trajectories only contributed 5.6%-11.1% of the total dominant transport pathways, while those in trajectories from the northwest were highest during HPEs Ι and Ⅱ. The highest contributions of air masses from short distances were found during HPE Ⅲ, of 77.8%, while they were only 65.6% and 47.8% during HPEs Ι and II, respectively. More attention should be given to transport pathways within the short distance from Jinan. Therefore, enhancing regional cooperation in Jinan and surrounding regions (particularly in the south, southeast, northwest, west, and north) is critical for improving air quality in the North China Plain.

The Origin and Molecular Epidemiology of Dengue Fever in Hainan Province, China, 2019.

There was an outbreak of Dengue fever on September 5, 2019, in Hainan Province, which has not been endemic for 28 years. We aim to describe the clinical and epidemiological features of the 2019 outbreak in Hainan Province and identify the cause. All type 1 Dengue fever cases that occurred in this outbreak of Hainan exhibited mild clinical symptoms. The epidemiological investigations indicate that the outbreak might originate from workers in the Xiuying area, Haikou City, form a concentrated outbreak, and then spread out. Bayesian phylogenies results and epidemiological data were used to infer a likely series of events for the dengue virus's potential spread and trace the possible sources. The strains' sequences were close to a sequence from the nearby Guangdong province, supporting the hypothesis that the dengue virus was imported from Guangdong province and then spread across Hainan province. Furthermore, it is interesting that two other strains didn't group with this cluster, suggesting that additional introduction pathways might exist. The study indicated that the dengue fever epidemic presented two important modes in Hainan. Firstly, epidemics prevalence was caused by imported cases, and then endogenous epidemics broke out in the natural epidemic focus.