Identifying the habitat suitability of Pteris vittata in China and associated key drivers using machine learning models.

Pteris vittata (P. vittata) possesses significant potential in remediating arsenic (As) soil pollution. Understanding the habitat suitability of P. vittata in China and pinpointing the key drivers that influence its distribution can facilitate the identification of optimal areas for using P. vittata as a remediation tool for As-polluted soils. In this study, a comparative analysis was conducted using ten machine learning models to assess the habitat suitability of P. vittata based on 744 specimen records and 20 environmental factors. The key drivers affecting the distribution of P. vittata were also investigated based on the optimal model. The results indicate that the XGBOOST model was the most reliable and stable, achieving a coefficient of determination of 0.95. Approximately 24.47 % of China's land area was identified as suitable for P. vittata. Particularly, it was predominantly found in Hainan (45.9 %), Guangxi (92.96 %), Guangdong (91.68 %), Hunan (91.26 %), Guizhou (90.83 %), Chongqing (88.17 %), Fujian (85.70 %), Yunnan (77.44 %), Jiangxi (73.99 %) and Zhejiang (57.05 %). Furthermore, this study pinpointed the lowest temperature, annual temperature range, and mining density as key drivers, contributing 45.9 %, 31.9 %, and 7.2 %, respectively. Spatial correlation analysis revealed a significant correlation between mining density and the habitat distribution of P. vittata (Moran' I = 0.519). This study confirmed that both natural factors and anthropogenic activities affect the distribution of P. vittata and provided valuable insights for optimizing the application of P. vittata in soil phytoremediation and reclamation.

Arsenic release during groundwater recharge and effects of coexisting ions in a typical inland basin with high arsenic concentration: Modeling and batch experiment.

Groundwater recharge is a viable solution to groundwater overexploitation. However, the injection of recharge water may break the dissolution balance and induce the release of trace elements especially arsenic (As), which has been identified in river deltas. Only a few studies have been conducted in inland basins with high As concentration, high pH, and low Eh. Aiming to analyze As release with groundwater recharge in inland high-As regions and determine the effects of coexisting ions in recharge water, this study established PHase Equilibria Calculation (PHREEQC) models using rainwater and groundwater data from three inland sedimentary basins with slow groundwater flow in semi-arid regions. The simulations fitted with the batch experiments, achieving an R-squared (R2) of 0.98. The coexisting ions in the recharge water significantly affected As release during recharge. Ca2+ inhibited the release of total arsenic (Total-As) by increasing the surface charge of iron oxides. NO3- inhibited Total-As release by promoting the conversion of trivalent As into pentavalent As. Conversely, HCO3- facilitated As release by competing with arsenate for adsorption sites. On the basis of the modeling and batch experiment results, Total-As release with groundwater recharge was predicted. The results indicated that the high Ca2+ concentration in the recharge water inhibited the As release by 83.5 %, which can be used as a strategy to control As release during groundwater recharge in high-As inland basins.

Accurate prediction of spatial distribution of soil potentially toxic elements using machine learning and associated key influencing factors identification: A case study in mining and smelting area in southwestern China.

Accurate prediction of spatial distribution of potentially toxic elements (PTEs) is crucial for soil pollution prevention and risk control. Achieving accurate prediction of spatial distribution of soil PTEs at a large scale using conventional methods presents significant challenges. In this study, machine learning (ML) models, specially artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB), were used to predict spatial distribution of soil PTEs and identify associated key factors in mining and smelting area located in Yunnan Province, China, under the three scenarios: (1) natural + socioeconomic + spatial datasets (NS), (2) NS + irrigation pollution index (IPI) datasets, (3) NS + IPI + deposition (DEPO) datasets. The results highlighted the combination of NS+IPI+DEPO yielded the highest predictive accuracy across ML models. Particularly, XGB exhibited the highest performance for As (R2 =0.7939), Cd (R2 =0.6679), Cu (R2 =0.8519), Pb (R2 =0.8317), and Zn (R2 =0.7669), whereas RF performed the best for Ni (R2 =0.7146). The feature importance and Shapley additive explanation (SHAP) analysis revealed that DEPO and IPI were the pivotal factors influencing the distribution of soil PTEs. Our findings highlighted the important role of DEPO in spatial distribution prediction of soil PTEs, which has often been ignored in previous studies.

Exosomes from umbilical cord mesenchymal stromal cells promote the collagen production of fibroblasts from pelvic organ prolapse.

BACKGROUND: Pelvic organ prolapse (POP) involves pelvic organ herniation into the vagina due to pelvic floor tissue laxity, and vaginal structure is an essential factor. In POP, the vaginal walls exhibit abnormal collagen distribution and decreased fibroblast levels and functions. The intricate etiology of POP and the prohibition of transvaginal meshes in pelvic reconstruction surgery present challenges in targeted therapy development. Human umbilical cord mesenchymal stromal cells (hucMSCs) present limitations, but their exosomes (hucMSC-Exo) are promising therapeutic tools for promoting fibroblast proliferation and extracellular matrix remodeling. AIM: To investigate the effects of hucMSC-Exo on the functions of primary vaginal fibroblasts and to elucidate the underlying mechanism involved. METHODS: Human vaginal wall collagen content was assessed by Masson's trichrome and Sirius blue staining. Gene expression differences in fibroblasts from patients with and without POP were assessed via RNA sequencing (RNA-seq). The effects of hucMSC-Exo on fibroblasts were determined via functional experiments in vitro. RNA-seq data from fibroblasts exposed to hucMSC-Exo and microRNA (miRNA) sequencing data from hucMSC-Exo were jointly analyzed to identify effective molecules. RESULTS: In POP, the vaginal wall exhibited abnormal collagen distribution and reduced fibroblast 1 quality and quantity. Treatment with 4 or 6 µg/mL hucMSC-Exo suppressed inflammation in POP group fibroblasts, stimulated primary fibroblast growth, and elevated collagen I (Col1) production in vitro. High-throughput RNA-seq of fibroblasts treated with hucMSC-Exo and miRNA sequencing of hucMSC-Exo revealed that abundant exosomal miRNAs downregulated matrix metalloproteinase 11 (MMP11) expression. CONCLUSION: HucMSC-Exo normalized the growth and function of primary fibroblasts from patients with POP by promoting cell growth and Col1 expression in vitro. Abundant miRNAs in hucMSC-Exo targeted and downregulated MMP11 expression. HucMSC-Exo-based therapy may be ideal for safely and effectively treating POP.

Potential in treating arsenic-contaminated water of the biochars produced from hyperaccumulator Pteris vittata and its environmental safety.

In this study, biochar derived from pyrolyzed aboveground parts of Pteris vittata (P. vittata) was modified with iron(Fe) and applied to aqueous solutions containing arsenite (As[III]) or arsenate (As[V]) for remediation purposes. The adsorption efficiency, biochar characteristics pre- and post-adsorption, microscopic As distribution, and As morphology were analyzed. Additionally, the potential and leaching safety of P. vittata biochar for As-contaminated water remediation were evaluated. Results indicated that P. vittata biochar contained oxygen-containing functional groups and aromatic structures. Modification with Fe increased specific surface area and total pore volume. Unmodified P. vittata biochar displayed low adsorption of As(III) and As(V), while Fe modification significantly enhanced As adsorption capacity and reduced As leaching by 69%-89%. Maximum adsorption capacities of Fe-modified P. vittata biochar for As(III) and As(V) were 7.64 and 10.2 mg/g, respectively, as determined by Langmuir fitting. The superior adsorption efficiency of As(V) over As(III) by Fe-modified biochar was attributed to better electrostatic interaction with the adsorbent. Analysis revealed similar As species in P. vittata biochar before and after adsorption, with a significant presence of As(III). Remarkably, As in P. vittata remained highly stable during pyrolysis and adsorption, possibly due to strong Fe-As binding. Fe-modified P. vittata biochar shows promise for application, but further pretreatment may be necessary to achieve optimal results.

Characterization of Ssc, an N-acetylgalactosamine-containing Staphylococcus aureus surface polysaccharide.

Whole genome sequencing has revealed that the genome of Staphylococcus aureus possesses an uncharacterized 5-gene operon (SAOUHSC_00088-00092 in strain 8325 genome) that encodes factors with functions related to polysaccharide biosynthesis and export, indicating the existence of a new extracellular polysaccharide species. We designate this locus as ssc for staphylococcal surface carbohydrate. We found that the ssc genes were weakly expressed and highly repressed by the global regulator MgrA. To characterize Ssc, Ssc was heterologously expressed in Escherichia coli and extracted by heat treatment. Ssc was also conjugated to AcrA from Campylobacter jejuni in E. coli using protein glycan coupling technology (PGCT). Analysis of the heat-extracted Ssc and the purified Ssc-AcrA glycoconjugate by tandem mass spectrometry revealed that Ssc is likely a polymer consisting of N-acetylgalactosamine. We further demonstrated that the expression of the ssc genes in S. aureus affected phage adsorption and susceptibility, suggesting that Ssc is surface-exposed. IMPORTANCE: Surface polysaccharides play crucial roles in the biology and virulence of bacterial pathogens. Staphylococcus aureus produces four major types of polysaccharides that have been well-characterized. In this study, we identified a new surface polysaccharide containing N-acetylgalactosamine (GalNAc). This marks the first report of GalNAc-containing polysaccharide in S. aureus. Our discovery lays the groundwork for further investigations into the chemical structure, surface location, and role in pathogenesis of this new polysaccharide.

Contrasting impacts of fertilization on topsoil and subsoil greenhouse gas fluxes in a thinned Chinese fir plantation.

Globally, forest soils are considered as important sources and sinks of greenhouse gases (GHGs). However, most studies on forest soil GHG fluxes are confined to the topsoils (above 20 cm soil depths), with only very limited information being available regarding these fluxes in the subsoils (below 20 cm soil depths), especially in managed forests. This limits deeper understanding of the relative contributions of different soil depths to GHG fluxes and global warming potential (GWP). Here, we used a concentration gradient-based method to comprehensively investigate the effects of thinning intensity (15% vs. 35%) and nutrient addition (no fertilizer vs. NPK fertilizers) on soil GHG fluxes from the 0-40 cm soil layers at 10 cm depth intervals in a Chinese fir (Cunninghamia lanceolata) plantation. Results showed that forest soils were the sources of CO2 and N2O, but the sinks of CH4. Soil GHG fluxes decreased with increasing soil depth, with the 0-20 cm soil layers identified as the dominant producers of CO2 and N2O and consumers of CH4. Thinning intensity did not significantly affect soil GHG fluxes. However, fertilization significantly increased CO2 and N2O emissions and CH4 uptake at 0-20 cm soil layers, but decreased them at 20-40 cm soil layers. This is because fertilization alleviated microbial N limitation and decreased water filled pore space (WFPS) in topsoils, while it increased WFPS in subsoils, ultimately suggesting that soil WFPS and N availability (especially NH4+-N) were the predominant regulators of GHG fluxes along soil profiles. Generally, there were positive interactive effects of thinning and fertilization on soil GHG fluxes. Moreover, the 35% thinning intensity without fertilization had the lowest GWP among all treatments. Overall, our results suggest that fertilization may not only cause depth-dependent effects on GHG fluxes within soil profiles, but also impede efforts to mitigate climate change by promoting GHG emissions in managed forest plantations.

Diagnostic features emerging in near-infrared reflectance spectroscopy for low petroleum hydrocarbon pollution after spectral subtraction.

Diagnostic features in near-infrared reflectance spectroscopy (NIRS) are the foundation of knowledge-based approach of petroleum hydrocarbon determination. However, a significant challenge arises when analyzing samples with low levels of petroleum hydrocarbon pollution, as they often lack distinctive diagnostic features in their sample NIRS spectra, limiting the effectiveness of this approach. To address this issue, we have developed a technical workflow for diagnostic spectrum construction and parameterization based on spectral subtraction. This method was applied on a set of NIRS spectra from soil samples that were contaminated with petroleum hydrocarbons (ranged between 178 and 1716 mg/kg of total petroleum hydrocarbon). Then two diagnostic features for low-level petroleum hydrocarbon pollution were found: (1) An overall downward concave emerged on diagnostic spectrum within both 2290-2370 nm and 1700-1780 nm for all low pollution levels even below 200 mg/kg; (2) An indicative pattern of asymmetric "W-shaped" double absorption valley occurred for those exceeding 1000 mg/kg, and its valleys located near 2310 nm, 2348 nm or 1727 nm, 1762 nm stably. These two features on diagnostic spectrum could be parameterized to detect, and the detection limit was at least about 10-50 times lower than that based on sample spectrum. These findings update our understanding on the detectability of spectral response from low petroleum hydrocarbon pollution, and widely extend the application of knowledge-based NIRS approach in either field detection or remote sensing identification for environmental management.

Influence of biochar on the arsenic phytoextraction potential of Pteris vittata in soils from an abandoned arsenic mining site.

Biochar (BC) has a strong potential for activating arsenic (As) in soil; thus, the phytoremediation efficiency of As-polluted soils is enhanced with Pteris vittata L. A pot experiment was conducted to investigate the potential of BC to assist in phytoremediation with P. vittata. The effects of BC on physicochemical properties, available As, enzyme activities, and the bacterial community in the rhizosphere soil were investigated, and the biomass, physiology, and As uptake of P. vittata were analyzed. The results indicated that applying BC facilitated available As in the P. vittata rhizosphere soil, and the phytoremediation efficiency percentage increased in the As-polluted soils, such as 3.80% and 8.01% under the 2% and 5% BC treatments compared to the control, respectively. Phytoremediation with P. vittata and BC significantly improved soil organic matter content, available N, P, and K, enzyme activities, and the bacterial community. BC promoted Streptomyces (26.6-54.2%) and Sphingomonas (12.3-30.8%) abundance which regulated the growth and As uptake by P. vittata. Moreover, applying BC increased the biomass, and As uptake by P. vittata. Overall, BC strengthened the phytoremediation of As-polluted soils by improving soil pH, nutrient concentrations, enzyme activities, bacterial community structure, and soil arsenic activation, growth, and absorption by P. vittata.

Validation of phenomenon and cross-sectional investigation of predictors for a post-COVID-19 surge of osteoporosis outpatients in China.

An unexpected surge of osteoporosis outpatients occurred after COVID-19 lockdown was lifted in China. To confirm the observation and identify possible reasons driving patients care seeking behaviors post-pandemic, we compared the outpatient volumes at the osteoporosis clinic in January through May, 2019-2022 and surveyed seven osteoporosis specialists across China to validate the phenomenon before devising an online questionnaire to collect patients' characters and physical activity levels. Univariate and binary logistic regression analyses were calculated to identify predictors of post-lockdown care-seeking. We received 480 valid responses, including 397 (82.7%) patients having visited the clinic after lockdown and 83 (17.3%) having not. Four significant predictors were identified, including being female, experiencing pain, aggravating symptoms, and heightened anxiety during lockdown (P < 0.05). Both groups experienced lower physical activity levels during lockdown, which however was not a significant predictor (P = 0.317). The surge in osteoporosis outpatient visits after COVID-19 lockdown suggests vast latent demand for osteoporosis care accumulated during the pandemic. Four significant factors predict post-lockdown outpatient care-seeking, including being female, experiencing pain and aggravating symptoms, and heightened anxiety levels. Though physical activity levels decreased during lockdown, it failed to predict care-seeking. This demonstrates resilience of osteoporosis patients to resume regular care despite disruption and stress the substantial backlog of unmet healthcare needs.

Intercropping of Pteris vittata and maize on multimetal contaminated soil can achieve remediation and safe agricultural production.

Soil contamination by multimetals is widespread. Hyperaccumulator-crop intercropping has been confirmed to be an effective method for arsenic (As)- or cadmium (Cd)-contaminated soil that can achieve soil cleanup and agricultural production. However, the influencing factors and response of hyperaccumulator-crop intercropping to multimetal-contaminated soil are still unclear. In this study, intercropping of the As hyperaccumulator Pteris vittata and maize was conducted on two typical types of multimetal-contaminated soil, namely, Soil A contaminated by As, Cd, and lead (Pb) and Soil B contaminated by As, Cd, and chromium (Cr). Intercropping reduced As, Cd, and Pb in the maize grains by 60 %, 66.7 %, and 20.4 %, respectively. The concentrations of As, Cd, Pb, and Cr in P. vittata increased by 314 %, 300 %, 447.3 %, and 232.6 %, respectively, relative to their concentrations in the monoculture plants. Two soils with different levels of contamination showed that higher heavy metal content might diminish the ability of intercropping to reduce soil heavy metal risk. No notable difference in soil microbial diversity was found between the intercropped and monocultured plants. The composition of microbial communities of intercropping groups were more similar to those of monoculture P. vittata on two different soils (Soils A and B). An imbalance between the amount of As taken up by the plants and the reduction in As in the soil was observed, and this imbalance may be related to watering, As leaching, and heterogeneity of soil As distribution. Reducing the risk resulting from As leaching and enhancing the efficiency of phytoextraction should be emphasized in remediation practices.

Sex difference in the association between BMI and cognitive impairment in Chinese older adults.

BACKGROUND: The association between body mass index (BMI) and cognitive impairment (CI) has been the subject of extensive research, yet the precise dose-response effects remain undefined. METHODS: Older adults were selected from the 2011/2012 survey at baseline and the new recruits from the 2014 and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Multiple logistic regression models were used to evaluate the association between BMI categories and CI, and Restricted Cubic Spline (RCS) was used to explore the nonlinear relationship between BMI and CI. RESULTS: The study included 29,380 older adults aged from 65 to 117 years, with an average age of 82 years. Of these, 13,465 were men, and 5359 exhibited cognitive impairment. The logistic model indicated that in female participants, being underweight was positively correlated with CI (OR:1.32; 95%CI 1.20-1.46), whereas being overweight was inversely correlated with CI (OR:0.86; 95%CI 0.75-0.99), and we didn't find any association between BMI category and CI in male participants. RCS modeling revealed a U-shaped relationship between BMI and CI. When stratified by sex, men exhibited a similar trend, with the lowest risk at a BMI of 22.774 kg/ m2, while women had the lowest risk of CI at a BMI of 24.817 kg/ m2. LIMITATION: This was a cross-sectional study, it cannot provide information on causal relationships. CONCLUSION: A U-shaped relationship was observed between BMI and CI in older adults, more pronounced in the male population, suggesting that male older adults may need to manage their BMI more rigorously.

Identifying driving factors of soil heavy metal at the mining area scale: Methods and practice.

Identifying driving factors is of great significance for understanding the mechanisms of soil pollution. In this study, a data processing method for driving factors was analyzed to explore the genesis of Arsenic (As) pollution in mining areas. The wind field that affects the atmospheric diffusion of pollutants was simulated using the standard k-ε model. Machine learning and GeoDetector methods were used to identify the primary driving factors. The results showed that the prediction performances of the three machine learning models were improved after data processing. The R2 values of random forest (RF), support vector machine, and artificial neural network increased from 0.45, 0.69, and 0.24 to 0.55, 0.76, and 0.52, respectively. The importance of wind increased from 20.85% to 26.22%. The importance of distance to the smelter plant decreased from 43.26% to 33.19% in the RF model. The wind's driving force (q value) increased from 0.057 to 0.235 in GeoDetector. The average value of historical atmospheric dust reached 534.98 mg/kg, indicating that atmospheric deposition was an important pathway for As pollution. The outcome of this study can provide a direction to clarify the mechanisms responsible for soil pollution at the mining area scale.

[Soil Pollution Prediction from Enterprise for Coking Industry in China Based on Machine Learning and Scenario Analysis].

Owing to the lack of sequential monitoring data of soil pollutants in coking industry enterprises, it is hard to accurately predict their soil pollution. To predict the trend of soil pollution of coking industry enterprises in the future, a prediction model should be developed using machine learning based on the influencing factors. A total of 13 potential factors were selected from the enterprise characteristics, enterprise management level, pollutant characteristics, and natural factors, and the main controlling factors were identified. On this basis, the prediction models were developed using a support vector machine, BP neural network model, decision tree model, and logistic model, and then the pollution situation of enterprises in the coking industry in 2025 and 2030 was predicted under different scenarios. The results indicated that time of service for the enterprise, time of establishment for the enterprise, the environmental illegal record, soil clay, and annual wind speed were the major controlling factors of soil pollution of enterprises in the coking industry. Compared with the support vector machine, BP neural network model, and decision tree model, the logistic model had a robust performance index, with an area under the receiver operating characteristic curve of 0.91. The accurate rate and recall rate were 84% and 88%, respectively. Under the optimistic scenario, there will be 1599 and 1695 plots with a high probability of pollution in the coking industry in 2025 and 2030, respectively; under the pessimistic scenario, there will be 1671 and 1715 plots with a high probability of pollution in the coking industry in 2025 and 2030, respectively. The results of this study provided a scientific basis for soil environmental remediation and eco-environmental strategy development for the coking industry.

Rhizosphere environmental factors regulated the cadmium adsorption by vermicompost: Influence of pH and low-molecular-weight organic acids.

Vermicompost is a promising amendment for immobilization of cadmium (Cd) in soils; however, its effectiveness can be influenced by rhizosphere environment conditions, such as pH and the presence of low-molecular-weight organic acids (LMWOAs). In this study, a batch experiment was conducted to examine the characteristics of Cd adsorption by vermicompost at different pH (pH = 3, 5, and 7) and after the addition of different LMWOAs (oxalic acid; citric acid; malic acid). Furthermore, a series of morphology and structural analyses were conducted to elucidate the mechanisms of observed effects. The results showed that the adsorption capacity of vermicompost for Cd increased as pH increased, and chemisorption dominated the adsorption process. Changes in pH altered adsorption performance by affecting the -OH groups of alcohol/phenol and the -CH2 groups of aliphatics. Further, the addition of oxalic acid promoted Cd adsorption, and the effect was concentration dependent. Modifying the verimicompost surface with more adsorption sites might be the main reason. Conversely, citric acid and malic acid showed the ability to inhibit Cd adsorption by vermicompost. Citric acid caused a blocking effect by covering flocculent substances on the vermicompost surface while reducing surface adsorption sites by dissolving mineral components such as iron oxides. However, the action of malic acid did not appear to be related to changes in morphology or the structure of vermicompost. Overall, the results of this study partially explain the limited effectiveness of Cd immobilization within the rhizosphere by vermicompost, and provide theoretical support for regulating rhizosphere environments to improve the effectiveness of vermicompost immobilization of Cd.

Research advances in mechanisms of arsenic hyperaccumulation of Pteris vittata: Perspectives from plant physiology, molecular biology, and phylogeny.

Pteris vittata, as the firstly discovered arsenic (As) hyperaccumulator, has great application value in As-contaminated soil remediation. Currently, the genes involved in As hyperaccumulation in P. vittata have been mined continuously, while they have not been used in practice to enhance phytoremediation efficiency. Aiming to better assist the practice of phytoremediation, this review collects 130 studies to clarify the progress in research into the As hyperaccumulation process in P. vittata from multiple perspectives. Antioxidant defense, rhizosphere activities, vacuolar sequestration, and As efflux are important physiological activities involved in As hyperaccumulation in P. vittata. Among related 19 genes, PHT, TIP, ACR3, ACR2 and HAC family genes play essential roles in arsenate (AsⅤ) transport, arsenite (AsⅢ) transport, vacuole sequestration of AsⅢ, and the reduction of AsⅤ to AsⅢ, respectively. Gene ontology enrichment analysis indicated it is necessary to further explore genes that can bind to related ions, with transport activity, or with function of transmembrane transport. Phylogeny analysis results implied ACR2, HAC and ACR3 family genes with rapid evolutionary rate may be the decisive factors for P. vittata as an As hyperaccumulator. A deeper understanding of the As hyperaccumulation network and key gene components could provide useful tools for further bio-engineered phytoremediation.

The novel HLA-B*27:267 allele, identified by Sanger dideoxy nucleotide sequencing in a Chinese individual.

HLA-B*27:267 differs from HLA-B*27:04:01 by one nucleotide in exon 2.

Clinical Characteristics of Tracheobronchopathia Osteochondroplastica: A Retrospective Study of 33 Patients.

Introduction: Tracheobronchopathia osteochondroplastica (TO) is a relatively rare benign tracheobronchial disease, which is often misdiagnosed or missed. To date, there is no specific treatment for TO. The aim of this study was to investigate the clinical manifestations, imaging features, bronchoscopy results, pathological findings, and diagnostic points of TO. Patients and methods: A total of 33 patients diagnosed with TO were enrolled. Clinical data were collected using retrospective methods in the period from January 2021 and November 2022. Descriptive analysis was used. Results: Patients included 17 (51.5%) male and 16 (48.5%) female, with a median age of 54 years. The main clinical manifestations included cough in 15 cases, fever in 6 cases, chest tightness in 4 cases, haemoptysis in 3 cases, and chest pain in 4 cases. The time from the onset of symptoms to diagnosis was 1 week to 96 months. Some patients were diagnosed with other lung diseases, including 16 patients with tuberculosis, 2 patients with lung cancer, 3 patients with nontuberculous mycobacteriosis, 3 patients with tuberculous pleurisy, 2 patients with bronchiectasis, and 1 patient with pneumonia. Chest computed tomography (CT) scan demonstrated calcified nodules in 10 (30.3%) patients. In bronchoscopy, entire tracheal involvement was found in 21 (63.6%) patients, 12 (36.4%) patients were found to have involvement of only part of the trachea. The patients were divided into three groups according to the bronchoscopic presentation, the largest proportion was stage II (19/33), followed by stage I (8/33) and stage III (6/33). Histopathological findings showed squamous metaplasia, cartilaginous, and bony tissues. Conclusion: TO is a slowly progressing disease with non-specific clinical symptoms and a low positive rate of imaging diagnosis, making it susceptible to misdiagnosis and missed diagnosis. The disease needs to be diagnosed by combining imaging features, fiberoptic bronchoscopy, and pathological findings.

Phytoextraction of As by Pteris vittata L. assisted with municipal sewage sludge compost and associated mechanism.

Pteris vittata L. (PV), an arsenic (As) hyperaccumulator, has a potential to extract As from As-polluted soils. Since available As in soils can be taken up by PV, As fraction variation associated rhizosphere environmental characteristics caused by municipal sewage sludge compost (MSSC) could provide possible to strengthen As phytoextraction by PV. In this study, the mechanism of phytoextraction of PV aided by MSSC was revealed from aspect of environmental characteristics of rhizosphere soils and physiological properties of PV. The effect of MSSC on available As in soils was investigated by soil incubation experiment. Furthermore, the influences of MSSC on enzymes activities, communities of soil bacteria and fungi, As concentrations, and As fraction in rhizosphere soils of PV were explored, and then the biomass and As accumulation of PV were examined by greenhouse pot experiments. After 90 days, available As in soil incubation experiment significantly increased by 32.63 %, 43.05 %, and 36.84 % under 2 %, 5 %, and 10 % treatment, respectively, compared with control treatment. Moreover, As concentrations in rhizosphere soils of PV under 2 %, 5 %, and 10 % treatment decreased by 4.62 %, 8.68 %, and 7.47 %, respectively, compared with control treatment. The available nutrients and enzyme activities in rhizosphere soils of PVs were improved under the MSSC treatment. Affected by MSSC, the dominant phylum and genus for both bacterial and fungal communities didn't change, but their relative abundance increased. Additionally, MSSC significantly increased biomass of PV with corresponding mean ranging from 2.82 to 3.42 g in shoot and 1.82 to 1.89 g in root, respectively. And the concentrations of As in shoot and root of PV treated by MSSC increased by 29.04 %-144.7 % and 26.34 %-81.78 %, respectively, in relative to control. The results of this study provided a basis for MSSC-strengthened phytoremediation for As-polluted soils.