Understanding the Ecological Robustness and Adaptability of the Gut Microbiome in Plastic-Degrading Superworms (Zophobas atratus) in Response to Microplastics and Antibiotics.

Recent discoveries indicate that several insect larvae are capable of ingesting and biodegrading plastics rapidly and symbiotically, but the ecological adaptability of the larval gut microbiome to microplastics (MPs) remains unclear. Here, we described the gut microbiome assemblage and MP biodegradation of superworms (Zophobas atratus larvae) fed MPs of five major petroleum-based polymers (polyethylene, polypropylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate) and antibiotics. The shift of molecular weight distribution, characteristic peaks of C═O, and metabolic intermediates of residual polymers in egested frass proved depolymerization and biodegradation of all MPs tested in the larval intestines, even under antibiotic suppression. Superworms showed a wide adaptation to the digestion of the five polymer MPs. Antibiotic suppression negatively influenced the survival rate and plastic depolymerization patterns. The larval gut microbiomes differed from those fed MPs and antibiotics, indicating that antibiotic supplementation substantially shaped the gut microbiome composition. The larval gut microbiomes fed MPs had higher network complexity and stability than those fed MPs and antibiotics, suggesting that the ecological robustness of the gut microbiomes ensured the functional adaptability of larvae to different MPs. In addition, Mantel's test indicated that the gut microbiome assemblage was obviously related to the polymer type, the plastic degradability, antibiotic stress, and larval survival rate. This finding provided novel insights into the self-adaptation of the gut microbiome of superworms in response to different MPs.

Molecular-Weight-Dependent Degradation of Plastics: Deciphering Host-Microbiome Synergy Biodegradation of High-Purity Polypropylene Microplastics by Mealworms.

The biodegradation of polypropylene (PP), a highly persistent nonhydrolyzable polymer, by Tenebrio molitor has been confirmed using commercial PP microplastics (MPs) (Mn 26.59 and Mw 187.12 kDa). This confirmation was based on the reduction of the PP mass, change in molecular weight (MW), and a positive Δδ13C in the residual PP. A MW-dependent biodegradation mechanism was investigated using five high-purity PP MPs, classified into low (0.83 and 6.20 kDa), medium (50.40 and 108.0 kDa), and high (575.0 kDa) MW categories to access the impact of MW on the depolymerization pattern and associated gene expression of gut bacteria and the larval host. The larvae can depolymerize/biodegrade PP polymers with high MW although the consumption rate and weight losses increased, and survival rates declined with increasing PP MW. This pattern is similar to observations with polystyrene (PS) and polyethylene (PE), i.e., both Mn and Mw decreased after being fed low MW PP, while Mn and/or Mw increased after high MW PP was fed. The gut microbiota exhibited specific bacteria associations, such as Kluyvera sp. and Pediococcus sp. for high MW PP degradation, Acinetobacter sp. for medium MW PP, and Bacillus sp. alongside three other bacteria for low MW PP metabolism. In the host transcriptome, digestive enzymes and plastic degradation-related bacterial enzymes were up-regulated after feeding on PP depending on different MWs. The T. molitor host exhibited both defensive function and degradation capability during the biodegradation of plastics, with high MW PP showing a relatively negative impact on the larvae.

Novel decision tree models predict the overall survival of patients with submandibular gland cancer.

BACKGROUND: While the accurate prediction of the overall survival (OS) in patients with submandibular gland cancer (SGC) is paramount for informed therapeutic planning, the development of reliable survival prediction models has been hindered by the rarity of SGC cases. The purpose of this study is to identify key prognostic factors for OS in SGC patients using a large database and construct decision tree models to aid the prediction of survival probabilities in 12, 24, 60 and 120 months. MATERIALS AND METHODS: We performed a retrospective cohort study using the Surveillance, Epidemiology and End Result (SEER) program. Demographic and peri-operative predictor variables were identified. The outcome variables overall survival at 12-, 24-, 60, and 120 months. The C5.0 algorithm was utilized to establish the dichotomous decision tree models, with the depth of tree limited within 4 layers. To evaluate the performances of the novel models, the receiver operator characteristic (ROC) curves were generated, and the metrics such as accuracy rate, and area under ROC curve (AUC) were calculated. RESULTS: A total of 1,705, 1,666, 1,543, and 1,413 SGC patients with a follow up of 12, 24, 60 and 120 months and exact survival status were identified from the SEER database. Predictor variables of age, sex, surgery, radiation, chemotherapy, tumor histology, summary stage, metastasis to distant lymph node, and marital status exerted substantial influence on overall survival. Decision tree models were then developed, incorporating these vital prognostic indicators. Favorable consistency was presented between the predicted and actual survival statuses. For the training dataset, the accuracy rates for the 12-, 24-, 60- and 120-month survival models were 0.866, 0.767, 0.737 and 0.797. Correspondingly, the AUC values were 0.841, 0.756, 0.725, and 0.774 for the same time points. CONCLUSIONS: Based on the most important predictor variables identified using the large, SEER database, decision tree models were established that predict OS of SGC patients. The models offer a more exhaustive evaluation of mortality risk and may lead to more personalized treatment strategies.

Biodegradation of various grades of polyethylene microplastics by Tenebrio molitor and Tenebrio obscurus larvae: Effects on their physiology.

Polyethylene (PE) is the most productive plastic product and includes three major polymers including high-density polyethylene (HDPE), linear low-density polyethylene (LLDPE) and low-density polyethylene (LDPE) variation in the PE depends on the branching of the polymer chain and its crystallinity. Tenebrio obscurus and Tenebrio molitor larvae biodegrade PE. We subsequently tested larval physiology, gut microbiome, oxidative stress, and PE degradation capability and degradation products under high-purity HDPE, LLDPE, and LDPE powders (<300 µm) diets for 21 days at 65 ± 5% humidity and 25 ± 0.5 °C. Our results demonstrated the specific PE consumption rates by T. molitor was 8.04-8.73 mg PE ∙ 100 larvae-1⋅day-1 and by T. obscurus was 7.68-9.31 for LDPE, LLDPE and HDPE, respectively. The larvae digested nearly 40% of the ingested three PE and showed similar survival rates and weight changes but their fat content decreased by 30-50% over 21-day period. All the PE-fed groups exhibited adverse effects, such as increased benzoquinone concentrations, intestinal tissue damage and elevated oxidative stress indicators, compared with bran-fed control. In the current study, the digestive tract or gut microbiome exhibited a high level of adaptability to PE exposure, altering the width of the gut microbial ecological niche and community diversity, revealing notable correlations between Tenebrio species and the physical and chemical properties (PCPs) of PE-MPs, with the gut microbiome and molecular weight change due to biodegradation. An ecotoxicological simulation by T.E.S.T. confirmed that PE degradation products were little ecotoxic to Daphnia magna and Rattus norvegicus providing important novel insights for future investigations into the environmentally-friendly approach of insect-mediated biodegradation of persistent plastics.

Prognostic factors and novel prediction models for overall survival of patients with submandibular gland cancer: A population-based retrospective cohort study.

Background: Accurately predicting the survival rate of submandibular gland cancer (SGC) is of significant importance for guiding treatment decision-making and improving patient outcomes. This study was aimed to identify the independent prognostic factors of overall survival (OS) in SGC patients, and develop novel prediction models to aid clinicians in predicting the survival probability. Materials and methods: Patients diagnosed with primary SGC after the year 2010 were extracted from SEER database and then randomly allocated into training and test samples in a 7:3 ratio. Uni- and multi-variable COX analyses were employed using the training sample to ascertain independent prognostic factors for OS. Subsequently, graphic and online dynamic nomograms were established basing on the independent prognostic factors. We utilized C-index, calibration curve, receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) value to evaluate the discrimination capacity and the consistency between predicted and actual survival. Results: A total of 527 SGC patients were included (369 assigned to training group and 158 assigned to test group). The multivariable COX analysis showed that age, sex, marital status, tumor histology, summary stage, metastases to bone, and tumor size were independently associated with OS. Novel graphical and online dynamic (URL: https://yangxg1209.shinyapps.io/overall_survival_submandibular_gland_tumor/) nomograms were established. The C-indices (training: 0.77, 95%CI 0.71-0.84; test: 0.77, 95%CI 0.68-0.85) indicate favorable discrimination ability of the model, and the calibration curves demonstrated favorable consistency between the predicted and actual survival rates. Conclusions: Our study identified the independent prognostic factors influencing OS in patients with SGC, and successfully established and validated novel nomograms, which provide accurate prediction of survival rates and allows for personalized risk assessment.

Novel machine-learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis.

BACKGROUND: Chondrosarcoma (CHS), a bone malignancy, poses a significant challenge due to its heterogeneous nature and resistance to conventional treatments. There is a clear need for advanced prognostic instruments that can integrate multiple prognostic factors to deliver personalized survival predictions for individual patients. This study aimed to develop a novel prediction tool based on recursive partitioning analysis (RPA) to improve the estimation of overall survival for patients with CHS. METHODS: Data from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed, including demographic, clinical, and treatment details of patients diagnosed between 2000 and 2018. Using C5.0 algorithm, decision trees were created to predict survival probabilities at 12, 24, 60, and 120 months. The performance of the models was assessed through confusion scatter plot, accuracy rate, receiver operator characteristic (ROC) curve, and area under ROC curve (AUC). RESULTS: The study identified tumor histology, surgery, age, visceral (brain/liver/lung) metastasis, chemotherapy, tumor grade, and sex as critical predictors. Decision trees revealed distinct patterns for survival prediction at each time point. The models showed high accuracy (82.40%-89.09% in training group, and 82.16%-88.74% in test group) and discriminatory power (AUC: 0.806-0.894 in training group, and 0.808-0.882 in test group) in both training and testing datasets. An interactive web-based shiny APP (URL: https://yangxg1209.shinyapps.io/chondrosarcoma_survival_prediction/) was developed, simplifying the survival prediction process for clinicians. CONCLUSIONS: This study successfully employed RPA to develop a user-friendly tool for personalized survival predictions in CHS. The decision tree models demonstrated robust predictive capabilities, with the interactive application facilitating clinical decision-making. Future prospective studies are recommended to validate these findings and further refine the predictive model.

Promotion of anaerobic biodegradation of azo dye RR2 by different biowaste-derived biochars: Characteristics and mechanism study by machine learning.

The addition of biochar resulted in a 31.5 % to 44.6 % increase in decolorization efficiency and favorable decolorization stability. Biochar promoted extracellular polymeric substances (EPS) secretion, especially humic-like and fulvic-like substances. Additionally, biochar enhanced the electron transfer capacity of anaerobic sludge and facilitated surface attachment of microbial cells. 16S rRNA gene sequencing analysis indicated that biochar reduced microbial species diversity, enriching fermentative bacteria such as Trichococcus. Finally, a machine learning model was employed to establish a predictive model for biochar characteristics and decolorization efficiency. Biochar electrical conductivity, H/C ratio, and O/C ratio had the most significant impact on RR2 anaerobic decolorization efficiency. According to the results, the possible mechanism of RR2 anaerobic decolorization enhanced by different types of biochar was proposed.

Effect and mechanism of Qingre Huashi decoction on drug-resistant Helicobacter pylori.

BACKGROUND: Helicobacter pylori (HP), the most common pathogenic microorganism in the stomach, can induce inflammatory reactions in the gastric mucosa, causing chronic gastritis and even gastric cancer. HP infection affects over 4.4 billion people globally, with a worldwide infection rate of up to 50%. The multidrug resistance of HP poses a serious challenge to eradication. It has been de-monstrated that compared to bismuth quadruple therapy, Qingre Huashi decoction (QHD) combined with triple therapy exhibits comparable eradication rates but with a lower incidence of adverse reactions; in addition, QHD can directly inhibit and kill HP in vitro. AIM: To explore the effect and mechanism of QHD on clinically multidrug-resistant and strong biofilm-forming HP. METHODS: In this study, 12 HP strains were isolated in vitro after biopsy during gastroscopy of HP-infected patients. In vitro, the minimum inhibitory concentration (MIC) values for clinical HP strains and biofilm quantification were determined through the E-test method and crystal violet staining, respectively. The most robust biofilm-forming strain of HP was selected, and QHD was evaluated for its inhibitory and bactericidal effects on the strain with strong biofilm formation. This assessment was performed using agar dilution, E-test, killing dynamics, and transmission electron microscopy (TEM). The study also explored the impact of QHD on antibiotic resistance in these HP strains with strong biofilm formation. Crystalline violet method, scanning electron microscopy, laser confocal scanning microscopy, and (p)ppGpp chromatographic identification were employed to evaluate the effect of QHD on biofilm in strong biofilm-forming HP strains. The effect of QHD on biofilm and efflux pump-related gene expression was evaluated by quantitative polymerase chain reaction. Non-targeted metabolomics with UHPLC-MS/MS was used to identify potential metabolic pathways and biomarkers which were different between the NC and QHD groups. RESULTS: HP could form biofilms of different degrees in vitro, and the intensity of formation was associated with the drug resistance of the strain. QHD had strong bacteriostatic and bactericidal effects on HP, with MICs of 32-64 mg/mL. QHD could inhibit the biofilm formation of the strong biofilm-forming HP strains, disrupt the biofilm structure, lower the accumulation of (p)ppGpp, decrease the expression of biofilm-related genes including LuxS, Spot, glup (HP1174), NapA, and CagE, and reduce the expression of efflux pump-related genes such as HP0605, HP0971, HP1327, and HP1489. Based on metabolomic analysis, QHD induced oxidative stress in HP, enhanced metabolism, and potentially inhibited relevant signaling pathways by upregulating adenosine monophosphate (AMP), thereby affecting HP growth, metabolism, and protein synthesis. CONCLUSION: QHD exerts bacteriostatic and bactericidal effects on HP, and reduces HP drug resistance by inhibiting HP biofilm formation, destroying its biofilm structure, inhibiting the expression of biofilm-related genes and efflux pump-related genes, enhancing HP metabolism, and activating AMP in HP.

New insights into substrates shaped nutrients removal, species interactions and community assembly mechanisms in tidal flow constructed wetlands treating low carbon-to-nitrogen rural wastewater.

A limited understanding of microbial interactions and community assembly mechanisms in constructed wetlands (CWs), particularly with different substrates, has hampered the establishment of ecological connections between micro-level interactions and macro-level wetland performance. In this study, CWs with distinct substrates (zeolite, CW_A; manganese ore, CW_B) were constructed to investigate the nutrient removal efficiency, microbial interactions, metabolic mechanisms, and ecological assembly for treating rural sewage with a low carbon-to-nitrogen ratio. CW_B showed higher removal of ammonia nitrogen and total nitrogen by about 1.75-6.75 % and 3.42-5.18 %, respectively, compared to CW_A. Candidatus_Competibacter (denitrifying glycogen-accumulating bacteria) was the dominant microbial genus in CW_A, whereas unclassified_f_Blastocatellaceae (involved in carbon and nitrogen transformation) dominated in CW_B. The null model revealed that stochastic processes (drift) dominated community assembly in both CWs; however, deterministic selection accounted for a higher proportion in CW_B. Compared to those in CW_A, the interactions between microbes in CW_B were more complex, with more key microbes involved in carbon, nitrogen, and phosphorus conversion; the synergistic cooperation of functional bacteria facilitated simultaneous nitrification-denitrification. Manganese ores favour biofilm formation, increase the activity of the electron transport system, and enhance ammonia oxidation and nitrate reduction. These results elucidated the ecological patterns exhibited by microbes under different substrate conditions thereby contributing to our understanding of how substrates shape distinct microcosms in CW systems. This study provides valuable insights for guiding the future construction and management of CWs.

Insight into effect of polyethylene microplastic on nitrogen removal in moving bed biofilm reactor: Focusing on microbial community and species interactions.

Microplastics (MPs) pollution has emerged as a global concern, and wastewater treatment plants (WWTPs) are one of the potential sources of MPs in the environment. However, the effect of polyethylene MPs (PE) on nitrogen (N) removal in moving bed biofilm reactor (MBBR) remains unclear. We hypothesized that PE would affect N removal in MBBR by influencing its microbial community. In this study, we investigated the impacts of different PE concentrations (100, 500, and 1000 µg/L) on N removal, enzyme activities, and microbial community in MBBR. Folin-phenol and anthrone colorimetric methods, oxidative stress and enzyme activity tests, and high-throughput sequencing combined with bioinformation analysis were used to decipher the potential mechanisms. The results demonstrated that 1000 µg/L PE had the greatest effect on NH4+-N and TN removal, with a decrease of 33.5 % and 35.2 %, and nitrifying and denitrifying enzyme activities were restrained by 29.5-39.6 % and 24.6-47.4 %. Polysaccharide and protein contents were enhanced by PE, except for 1000 µg/L PE, which decreased protein content by 65.4 mg/g VSS. The positive links of species interactions under 1000 µg/L PE exposure was 52.07 %, higher than under 500 µg/L (51.05 %) and 100 µg/L PE (50.35 %). Relative abundance of some metabolism pathways like carbohydrate metabolism and energy metabolism were restrained by 0.07-0.11 % and 0.27-0.4 %. Moreover, the total abundance of nitrification and denitrification genes both decreased under PE exposure. Overall, PE reduced N removal by affecting microbial community structure and species interactions, inhibiting some key metabolic pathways, and suppressing key enzyme activity and functional gene abundance. This paper provides new insights into assessing the risk of MPs to WWTPs, contributing to ensuring the health of aquatic ecosystems.

Ciprofloxacin affects nutrient removal in manganese ore-based constructed wetlands: Adaptive responses of macrophytes and microbes.

Ciprofloxacin (CIP) has received considerable attention in recent decades due to its high ecological risk. However, little is known about the potential response of macrophytes and microbes to varying levels of CIP exposure in constructed wetlands. Therefore, lab-scale manganese ore-based tidal flow constructed wetlands (MO-TFCWs) were operated to evaluate the responses of macrophytes and microbes to CIP over the long term. The results indicated that total nitrogen removal improved from 79.93% to 87.06% as CIP rose from 0 to 4 mg L-1. The chlorophyll content and antioxidant enzyme activities in macrophytes were enhanced under CIP exposure, but plant growth was not inhibited. Importantly, CIP exposure caused a marked evolution of the substrate microbial community, with increased microbial diversity, expanded niche breadth and enhanced cooperation among the top 50 genera, compared to the control (no CIP). Co-occurrence network also indicated that microorganisms may be more inclined to co-operate than compete. The abundance of the keystone bacterium (involved in nitrogen transformation) norank_f__A0839 increased from 0.746% to 3.405%. The null model revealed drift processes (83.33%) dominated the community assembly with no CIP and 4 mg L-1 CIP. Functional predictions indicated that microbial carbon metabolism, electron transfer and ATP metabolism activities were enhanced under prolonged CIP exposure, which may contribute to nitrogen removal. This study provides valuable insights that will help achieve stable nitrogen removal from wastewater containing antibiotic in MO-TFCWs.

Anthraquinones-based photocatalysis: A comprehensive review.

In recent years, there has been significant interest in photocatalytic technologies utilizing semiconductors and photosensitizers responsive to solar light, owing to their potential for energy and environmental applications. Current efforts are focused on enhancing existing photocatalysts and developing new ones tailored for environmental uses. Anthraquinones (AQs) serve as redox-active electron transfer mediators and photochemically active organic photosensitizers, effectively addressing common issues such as low light utilization and carrier separation efficiency found in conventional semiconductors. AQs offer advantages such as abundant raw materials, controlled preparation, excellent electron transfer capabilities, and photosensitivity, with applications spanning the energy, medical, and environmental sectors. Despite their utility, comprehensive reviews on AQs-based photocatalytic systems in environmental contexts are lacking. In this review, we thoroughly describe the photochemical properties of AQs and their potential applications in photocatalysis, particularly in addressing key environmental challenges like clean energy production, antibacterial action, and pollutant degradation. However, AQs face limitations in practical photocatalytic applications due to their low electrical conductivity and solubility-related secondary contamination. To mitigate these issues, the design and synthesis of graphene-immobilized AQs are highlighted as a solution to enhance practical photocatalytic applications. Additionally, future research directions are proposed to deepen the understanding of AQs' theoretical mechanisms and to provide practical applications for wastewater treatment. This review aims to facilitate mechanistic studies and practical applications of AQs-based photocatalytic technologies and to improve understanding of these technologies.

Spatiotemporal drivers of urban water pollution: Assessment of 102 cities across the Yangtze River Basin.

Effective management of large basins necessitates pinpointing the spatial and temporal drivers of primary index exceedances and urban risk factors, offering crucial insights for basin administrators. Yet, comprehensive examinations of multiple pollutants within the Yangtze River Basin remain scarce. Here we introduce a pollution inventory for urban clusters surrounding the Yangtze River Basin, analyzing water quality data from 102 cities during 2018-2019. We assessed the exceedance rates for six pivotal indicators: dissolved oxygen (DO), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), biochemical oxygen demand (BOD), total phosphorus (TP), and the permanganate index (CODMn) for each city. Employing random forest regression and SHapley Additive exPlanations (SHAP) analyses, we identified the spatiotemporal factors influencing these key indicators. Our results highlight agricultural activities as the primary contributors to the exceedance of all six indicators, thus pinpointing them as the leading pollution source in the basin. Additionally, forest coverage, livestock farming, chemical and pharmaceutical sectors, along with meteorological elements like precipitation and temperature, significantly impacted various indicators' exceedances. Furthermore, we delineate five core urban risk components through principal component analysis, which are (1) anthropogenic and industrial activities, (2) agricultural practices and forest extent, (3) climatic variables, (4) livestock rearing, and (5) principal polluting sectors. The cities were subsequently evaluated and categorized based on these risk components, incorporating policy interventions and administrative performance within each region. The comprehensive analysis advocates for a customized strategy in addressing the discerned risk factors, especially for cities presenting elevated risk levels.

Machine learning assisted combined systems of wastewater treatment plants with constructed wetlands optimal decision-making.

This study proposed an efficient framework for optimizing the design and operation of combined systems of wastewater treatment plants (WWTP) and constructed wetlands (CW). The framework coupled a WWTP model with a CW model and used a multi-objective evolutionary algorithm to identify trade-offs between energy consumption, effluent quality, and construction cost. Compared to traditional design and management approaches, the framework achieved a 27 % reduction in WWTP energy consumption or a 44 % reduction in CW cost while meeting strict effluent discharge limits for Chinese WWTP. The framework also identified feasible decision variable ranges and demonstrated the impact of different optimization strategies on system performance. Furthermore, the contributions of WWTP and CW in pollutant degradation were analyzed. Overall, the proposed framework offers a highly efficient and cost-effective solution for optimizing the design and operation of a combined WWTP and CW system.

Microplastics shaped performance, microbial ecology and community assembly in simultaneous nitrification, denitrification and phosphorus removal process.

The widespread use of microplastics (MPs) has led to an increase in their discharge to wastewater treatment plants. However, the knowledge of impact of MPs on macro-performance and micro-ecology in simultaneous nitrification, denitrification, and phosphorus removal (SNDPR) systems is limited, hampering the understanding of potential risks posed by MPs. This study firstly comprehensively investigated the performance, species interactions, and community assembly under polystyrene (PS) and polyvinyl chloride (PVC) exposure in SNDPR systems. The results showed under PS (1, 10 mg/L) and PVC (1, 10 mg/L) exposure, total nitrogen removal was reduced by 3.38-10.15 %. PS and PVC restrained the specific rates of nitrite and nitrate reduction (SNIRR, SNRR), as well as the activities of nitrite and nitrate reductase enzymes (NIR, NR). The specific ammonia oxidation rate (SAOR) and activity of ammonia oxidase enzyme (AMO) were reduced only at 10 mg/L PVC. PS and PVC enhanced the size of co-occurrence networks, niche breadth, and number of key species while decreasing microbial cooperation by 5.85-13.48 %. Heterogeneous selection dominated microbial community assembly, and PS and PVC strengthened the contribution of stochastic processes. PICRUSt prediction further revealed some important pathways were blocked by PS and PVC. Together, the reduced TN removal under PS and PVC exposure can be attributed to the inhibition of SAOR, SNRR, and SNIRR, the restrained activities of NIR, NR, and AMO, the changes in species interactions and community assembly mechanisms, and the suppression of some essential metabolic pathways. This paper offers a new perspective on comprehending the effects of MPs on SNDPR systems.

Biodegradation of polyethylene terephthalate by Tenebrio molitor: Insights for polymer chain size, gut metabolome and host genes.

Polyethylene terephthalate (PET or polyester) is a commonly used plastic and also contributes to the majority of plastic wastes. Mealworms (Tenebrio molitor larvae) are capable of biodegrading major plastic polymers but their degrading ability for PET has not been characterized based on polymer chain size molecular size, gut microbiome, metabolome and transcriptome. We verified biodegradation of commercial PET by T. molitor larvae in a previous report. Here, we reported that biodegradation of commercial PET (Mw 29.43 kDa) was further confirmed by using the δ13C signature as an indication of bioreaction, which was increased from - 27.50‰ to - 26.05‰. Under antibiotic suppression of gut microbes, the PET was still depolymerized, indicating that the host digestive enzymes could degrade PET independently. Biodegradation of high purity PET with low, medium, and high molecular weights (MW), i.e., Mw values of 1.10, 27.10, and 63.50 kDa with crystallinity 53.66%, 33.43%, and 4.25%, respectively, showed a mass reduction of > 95%, 86%, and 74% via broad depolymerization. Microbiome analyses indicated that PET diets shifted gut microbiota to three distinct structures, depending on the low, medium, and high MW. Metagenome sequencing, transcriptomic, and metabolic analyses indicated symbiotic biodegradation of PET by the host and gut microbiota. After PET was fed, the host's genes encoding degradation enzymes were upregulated, including genes encoding oxidizing, hydrolyzing, and non-specific CYP450 enzymes. Gut bacterial genes for biodegrading intermediates and nitrogen fixation also upregulated. The multiple-functional metabolic pathways for PET biodegradation ensured rapid biodegradation resulting in a half-life of PET less than 4 h with less negative impact by PET MW and crystallinity.

Cholesterol paradox in the community-living old adults: is higher better?

OBJECTIVE: To evaluate the associations of lipid indicators and mortality in Beijing Elderly Comprehensive Health Cohort Study. METHODS: A prospective cohort was conducted based on Beijing Elderly Comprehensive Health Cohort Study with 4499 community older adults. After the baseline survey, the last follow-up was March 31, 2021 with an average 8.13 years of follow-up. Cox proportional hazard model was used to estimate the hazard ratios (HR) with 95% CI for cardiovascular disease (CVD) death and all-cause death in associations with baseline lipid indicators. RESULTS: A total of 4499 participants were recruited, and the mean levels of uric acid, body mass index, systolic blood pressure, diastolic blood pressure, fasting plasma glucose, total cholesterol (TC), triglyceride, and low-density lipoprotein cholesterol (LDL-C) showed an upward trend with the increasing remnant cholesterol (RC) quarters (Ptrend < 0.05), while the downward trend was found in high-density lipoprotein cholesterol (HDL-C). During the total 36,596 person-years follow-up, the CVD mortality and all-cause mortality during an average 8.13 years of follow-up was 3.87% (95% CI: 3.30%-4.43%) and 14.83% (95% CI: 13.79%-15.86%) with 174 CVD death participants and 667 all-cause death participants. After adjusting for confounders, the higher level of TC (HR = 0.854, 95% CI: 0.730-0.997), LDL-C (HR = 0.817, 95% CI: 0.680-0.982) and HDL-C (HR = 0.443, 95% CI: 0.271-0.724) were associated with lower risk of CVD death, and the higher level of HDL-C (HR = 0.637, 95% CI: 0.501-0.810) were associated with lower risk of all-cause death. The higher level of RC (HR = 1.276, 95% CI: 1.010-1.613) increase the risk of CVD death. Compared with the normal lipid group, TC ≥ 6.20 mmol/L group and LDL-C ≥ 4.10 mmol/L group were no longer associated with lower risk of CVD death, while RC ≥ 0.80 mmol/L group was still associated with higher risk of CVD death. In normal lipid group, the higher levels of TC, LDL-C and HDL-C were related with lower CVD death. CONCLUSIONS: In community older adults, higher levels of TC and HDL-C were associated with lower CVD mortality in normal lipid reference range. Higher RC was associated with higher CVD mortality, which may be a better lipid indicator for estimating the CVD death risk in older adults.