AFP-MFL: accurate identification of antifungal peptides using multi-view feature learning.

Recently, peptide-based drugs have gained unprecedented interest in discovering and developing antifungal drugs due to their high efficacy, broad-spectrum activity, low toxicity and few side effects. However, it is time-consuming and expensive to identify antifungal peptides (AFPs) experimentally. Therefore, computational methods for accurately predicting AFPs are highly required. In this work, we develop AFP-MFL, a novel deep learning model that predicts AFPs only relying on peptide sequences without using any structural information. AFP-MFL first constructs comprehensive feature profiles of AFPs, including contextual semantic information derived from a pre-trained protein language model, evolutionary information, and physicochemical properties. Subsequently, the co-attention mechanism is utilized to integrate contextual semantic information with evolutionary information and physicochemical properties separately. Extensive experiments show that AFP-MFL outperforms state-of-the-art models on four independent test datasets. Furthermore, the SHAP method is employed to explore each feature contribution to the AFPs prediction. Finally, a user-friendly web server of the proposed AFP-MFL is developed and freely accessible at http://inner.wei-group.net/AFPMFL/, which can be considered as a powerful tool for the rapid screening and identification of novel AFPs.

MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism.

One of the main problems with the joint use of multiple drugs is that it may cause adverse drug interactions and side effects that damage the body. Therefore, it is important to predict potential drug interactions. However, most of the available prediction methods can only predict whether two drugs interact or not, whereas few methods can predict interaction events between two drugs. Accurately predicting interaction events of two drugs is more useful for researchers to study the mechanism of the interaction of two drugs. In the present study, we propose a novel method, MDF-SA-DDI, which predicts drug-drug interaction (DDI) events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. MDF-SA-DDI is mainly composed of two parts: multi-source drug fusion and multi-source feature fusion. First, we combine two drugs in four different ways and input the combined drug feature representation into four different drug fusion networks (Siamese network, convolutional neural network and two auto-encoders) to obtain the latent feature vectors of the drug pairs, in which the two auto-encoders have the same structure, and their main difference is the number of neurons in the input layer of the two auto-encoders. Then, we use transformer blocks that include self-attention mechanism to perform latent feature fusion. We conducted experiments on three different tasks with two datasets. On the small dataset, the area under the precision-recall-curve (AUPR) and F1 scores of our method on task 1 reached 0.9737 and 0.8878, respectively, which were better than the state-of-the-art method. On the large dataset, the AUPR and F1 scores of our method on task 1 reached 0.9773 and 0.9117, respectively. In task 2 and task 3 of two datasets, our method also achieved the same or better performance as the state-of-the-art method. More importantly, the case studies on five DDI events are conducted and achieved satisfactory performance. The source codes and data are available at https://github.com/ShenggengLin/MDF-SA-DDI.

DeepProSite: structure-aware protein binding site prediction using ESMFold and pretrained language model.

MOTIVATION: Identifying the functional sites of a protein, such as the binding sites of proteins, peptides, or other biological components, is crucial for understanding related biological processes and drug design. However, existing sequence-based methods have limited predictive accuracy, as they only consider sequence-adjacent contextual features and lack structural information. RESULTS: In this study, DeepProSite is presented as a new framework for identifying protein binding site that utilizes protein structure and sequence information. DeepProSite first generates protein structures from ESMFold and sequence representations from pretrained language models. It then uses Graph Transformer and formulates binding site predictions as graph node classifications. In predicting protein-protein/peptide binding sites, DeepProSite outperforms state-of-the-art sequence- and structure-based methods on most metrics. Moreover, DeepProSite maintains its performance when predicting unbound structures, in contrast to competing structure-based prediction methods. DeepProSite is also extended to the prediction of binding sites for nucleic acids and other ligands, verifying its generalization capability. Finally, an online server for predicting multiple types of residue is established as the implementation of the proposed DeepProSite. AVAILABILITY AND IMPLEMENTATION: The datasets and source codes can be accessed at https://github.com/WeiLab-Biology/DeepProSite. The proposed DeepProSite can be accessed at https://inner.wei-group.net/DeepProSite/.

Real-time laser speckle contrast imaging measurement during normothermic machine perfusion in pretransplant kidney assessment.

OBJECTIVES: Normothermic machine perfusion (NMP) provides a platform for pre-transplant kidney quality assessment that is essential for the use of marginal donor kidneys. Laser speckle contrast imaging (LSCI) presents distinct advantages as a real-time and noncontact imaging technique for measuring microcirculation. In this study, we aimed to assess the value of LSCI in visualizing renal cortical perfusion and investigate the additional value of dual-side LSCI measurements compared to single aspect measurement during NMP. METHODS: Porcine kidneys were obtained from a slaughterhouse and then underwent NMP. LSCI was used to measure one-sided cortical perfusion in the first 100 min of NMP. Thereafter, the inferior renal artery branch was occluded to induce partial ischemia and LSCI measurements on both ventral and dorsal sides were performed. RESULTS: LSCI fluxes correlated linearly with the renal blood flow (R2 = 0.90, p < 0.001). After renal artery branch occlusion, absence of renal cortical perfusion could be visualized and semiquantified by LSCI. The overall ischemic area percentage of the ventral and dorsal sides was comparable (median interquartile range [IQR], 38 [24-43]% vs. 29 [17-46]%, p = 0.43), but heterogenous patterns between the two aspects were observed. There was a significant difference in oxygen consumption (mean ± standard deviation [SD], 2.57 ± 0.63 vs. 1.83 ± 0.49 mLO2 /min/100 g, p < 0.001), urine output (median [IQR], 1.3 [1.1-1.7] vs. 0.8 [0.6-1.3] mL/min, p < 0.05), lactate dehydrogenase (mean ± SD, 768 ± 370 vs. 905 ± 401 U/L, p < 0.05) and AST (mean ± SD, 352 ± 285 vs. 462 ± 383 U/L, p < 0.01) before and after renal artery occlusion, while no significant difference was found in creatinine clearance, fractional excretion of sodium, total sodium reabsorption and histological damage. CONCLUSIONS: LSCI fluxes correlated linearly with renal blood flow during NMP. Renal cortical microcirculation and absent perfusion can be visualized and semiquantified by LSCI. It provides a relative understanding of perfusion levels, allowing for a qualitative comparison between regions in the kidney. Dual-side LSCI measurements are of added value compared to single aspect measurement and renal function markers.

High altitude Relieves transmission risks of COVID-19 through meteorological and environmental factors: Evidence from China.

Existing studies reported higher altitudes reduce the COVID-19 infection rate in the United States, Colombia, and Peru. However, the underlying reasons for this phenomenon remain unclear. In this study, regression analysis and mediating effect model were used in a combination to explore the altitudes relation with the pattern of transmission under their correlation factors. The preliminary linear regression analysis indicated a negative correlation between altitudes and COVID-19 infection in China. In contrast to environmental factors from low-altitude regions (<1500 m), high-altitude regions (>1500 m) exhibited lower PM2.5, average temperature (AT), and mobility, accompanied by high SO2 and absolute humidity (AH). Non-linear regression analysis further revealed that COVID-19 confirmed cases had a positive correlation with mobility, AH, and AT, whereas negatively correlated with SO2, CO, and DTR. Subsequent mediating effect model with altitude-correlated factors, such as mobility, AT, AH, DTR and SO2, suffice to discriminate the COVID-19 infection rate between low- and high-altitude regions. The mentioned evidence advance our understanding of the altitude-mediated COVID-19 transmission mechanism.

Evolution of RGF/GLV/CLEL Peptide Hormones and Their Roles in Land Plant Growth and Regulation.

Rooting is a key innovation during plant terrestrialization. RGFs/GLVs/CLELs are a family of secreted peptides, playing key roles in root stem cell niche maintenance and pattern formation. The origin of this peptide family is not well characterized. RGFs and their receptor genes, RGIs, were investigated comprehensively using phylogenetic and genetic analyses. We identified 203 RGF genes from 24 plant species, representing a variety of land plant lineages. We found that the RGF genes originate from land plants and expand via multiple duplication events. The lineage-specific RGF duplicates are retained due to their regulatory divergence, while a majority of RGFs experienced strong purifying selection in most land plants. Functional analysis indicated that RGFs and their receptor genes, RGIs, isolated from liverwort, tomato, and maize possess similar biological functions with their counterparts from Arabidopsis in root development. RGFs and RGIs are likely coevolved in land plants. Our studies shed light on the origin and functional conservation of this important peptide family in plant root development.

Effecting mechanisms of iron-rich sludge on ash fusion characteristics of coal with high ash fusion temperature under reducing atmosphere.

Co-gasification is crucial for large-scale clean conversion of coal and sludge. In this study, the effects of municipal sewage sludge (MSS, Fe2O3:48.11 %) and pharmaceutical sewage sludge (PSS, Fe2O3: 67.80 %) on ash fusion temperature (AFT) of high AFT Xiangyuan coal (XY) were explored using an AFT analysis, X-ray fluorescence spectrometry, X-ray diffraction, scanning electronic microscopy, and thermodynamics FactSage calculation. The results showed that when MSS or PSS ash mass ratios reached 20 % or 16 % (for XY mixtures, the mass ratio of MSS or PSS should be >5.81 wt% or 5.07 wt%), respectively, the AFT met the requirement of liquid-slag discharge for entrained-flow bed gasification. Under a reducing atmosphere (6:4, CO/CO2, volume ratio), Fe2+ destroyed the bridging-oxygen bonds in the network structure and generated low melting-point (MP) hercynite (FeAl2O4). This resulted in the AFT decreases in the XY mixtures with the additions of PSS or MSS. Meanwhile, the high calcium content (CaO: 13.40 %) easily reacted with Al2O3 and SiO2 and formed anorthite (CaAl2SiO8), which inhibited high-MP mullite formation and decreased the mixed XY AFT. With the increasing SS mass ratio, the surface of the ash sample and thermodynamic FactSage calculation were in good agreement with the experimental results.

Coupling experimental with simulation studies into the impact factors and reaction mechanism of sawdust char pressured hydrogasification on K-modified transition metal composite catalysts.

The utilization of biomass char was hindered by the low gasification activity due to thick ring structures and unclear gasification mechanism. Herein, the mechanism was elucidated by experimental and DFT to improve the activity. The results demonstrated that temperature increased the gasification activity but did not changed the order of gasification activity of samples. Pressure dominated the position of the highest point of instantaneous CH4 yield, and high pressure enhanced carbon conversion by 81.72 % and 7.32 times. Moreover, KNi exhibited an uppermost catalytic activity with the instantaneous CH4 yield 1.89 times higher than that of raw char at 750 °C. The formation of the CxNi structure lowered the activation barrier for the ring opening reaction. Possible transformation pathways of Ni species were as follows: Ni(NO3)2·6H2O â†’ NiO â†’ Ni. KNi changed the reaction pathways and the most energy-consuming step. The study could shed light on the hydrogasification reaction mechanism.

Effect of hydrothermal pH values on the morphology of special microspheres of lignin-based porous carbon and the mechanism of carbon dioxide adsorption.

The study reports the economic and sustainable syntheses of a lignin-based porous carbon (LPC) for CO2 capture application. The pH values of hydrothermal solution affected the polymerization and aromatization of spheroidization, with morphological changes from blocky to microsphere. In addition, the reliable mechanisms of CO2 adsorption were proposed by combining experiments with Gaussian16 simulations based on DFT. The electrostatic attraction of oxygen-containing functional groups and the diffusivity resistance of CO2 in the pores are the key factors for the CO2 adsorption. ​The carboxyl groups have the strongest electrostatic attraction to CO2. LPC-pH 1 has the highest carboxyl group content, possessing a CO2 adsorption capacity of up to 5.10 mmol/g at 0℃, 1 bar. Furthermore, CO2 diffusion resistance became a main factor as the adsorption temperature increases. The innovative combination of quantum chemical calculations and microscopic properties provides a viable pathway for an insight into the future control of lignin-based carbon formation.

Safety and efficacy of kidney transplantation in patients with aortoiliac stenosis: a retrospective cohort study.

BACKGROUND: The impact of aortoiliac occlusive disease on kidney transplantation remains unclear. This study aims to investigate the clinical outcomes of kidney transplant patients with aortoiliac atherosclerotic stenosis. METHODS: Retrospective data from our transplant center were used to identify patients undergoing kidney transplantation between January 2010 and December 2020. Aortoiliac atherosclerotic stenosis was screened and stratified by the Trans-Atlantic Inter-Society Consensus (TASC) II classification. The primary outcome was patient survival. Secondary outcomes were 90-day mortality, death-censored graft survival, graft function, and arterial complications. Propensity score matching was used to match all patients in the stenosis group with patients without stenosis sharing similar characteristics. RESULTS: The analysis included 655 patients, 524 without stenosis and 131 with aortoiliac stenosis (95 with TASC A/B stenosis and 36 with TASC C/D stenosis). Recipient age [median (IQR), 66 (60-70) vs. 66 (59-71) years; P =0.47], sex [male: 87 (66%) vs. 355 (68%), P =0.85], and comorbidities were comparable between the stenosis and no-stenosis groups. Forty-six (35%) patients with stenosis were symptomatic. Patient survival was significantly lower in the stenosis group compared with the no-stenosis group (TASC A/B: 30.6% vs. no-stenosis: 44.1%, P =0.013; TASC C/D: 11.4% vs. no-stenosis: 44.1%, P <0.001). The incidence rates of artery dissection, lower extremity ischemia, and acute thrombosis were significantly higher in the stenosis group ( P <0.001). However, death-censored graft survival (TASC A/B: 73.6% vs. no-stenosis: 72.9%, P =0.62; TASC C/D: 58.1% vs. no-stenosis: 72.9%, P =0.16) and graft function were comparable between the groups. CONCLUSIONS: Aortoiliac atherosclerotic stenosis significantly impacts patient survival but not graft survival. Our analyses suggest that patients with TASC A/B stenosis have prolonged survival and enhanced quality of life through kidney transplantation. However, for patients with TASC C/D stenosis, kidney transplantation improves quality of life without bringing survival benefits.

Biomass-based controllable morphology of carbon microspheres with multi-layer hollow structure for superior performance in supercapacitors.

The electrochemical properties of corn starch (CS)-based hydrothermal carbon microsphere (CMS) electrode materials for supercapacitor are closely related to their structures. Herein, cetyltrimethyl ammonium bromide (CTAB) was used as a soft template to form the corn starch (CS)-based carbon microspheres with radial hollow structure in the inner and middle layers by hydrothermal and sol-gel method. Due to the introduction of multi-layer hollow structure of carbon microsphere, more micropores were produced during CO2 activation, which increased the specific surface area and improved the capacitance performance. Compared to commercial activated carbon, the four different morphologies of corn starch CMS had better electrochemical performances. Consequently, the proposed CO2-(CTAB)-CS-CS exhibits a high discharge specific capacitance of 242.5F/g at 1 A/g in three-electrode system with 6 M KOH electrolyte, better than commercial activated carbon with 208.5F/g. Moreover, excellent stability is achieved for CO2-(CTAB)-CS-CS with approximately 97.14 % retention of the initial specific capacitance value after 10,000 cycles at a current density of 2 A/g, while the commercial activated carbon has 86.96 % retention. This implies that the corn starch-based multilayer hollow CMS could be a promising electrode material for high-performance supercapacitors.

Exploring the Conformational Ensembles of Protein-Protein Complex with Transformer-Based Generative Model.

Protein-protein interactions are the basis of many protein functions, and understanding the contact and conformational changes of protein-protein interactions is crucial for linking the protein structure to biological function. Although difficult to detect experimentally, molecular dynamics (MD) simulations are widely used to study the conformational ensembles and dynamics of protein-protein complexes, but there are significant limitations in sampling efficiency and computational costs. In this study, a generative neural network was trained on protein-protein complex conformations obtained from molecular simulations to directly generate novel conformations with physical realism. We demonstrated the use of a deep learning model based on the transformer architecture to explore the conformational ensembles of protein-protein complexes through MD simulations. The results showed that the learned latent space can be used to generate unsampled conformations of protein-protein complexes for obtaining new conformations complementing pre-existing ones, which can be used as an exploratory tool for the analysis and enhancement of molecular simulations of protein-protein complexes.

Pre-transplant kidney quality evaluation using photoacoustic imaging during normothermic machine perfusion.

Due to the shortage of kidneys donated for transplantation, surgeons are forced to use the organs with an elevated risk of poor function or even failure. Although the existing methods for pre-transplant quality evaluation have been validated over decades in population cohort studies across the world, new methods are needed as long as delayed graft function or failure in a kidney transplant occurs. In this study, we explored the potential of utilizing photoacoustic (PA) imaging during normothermic machine perfusion (NMP) as a means of evaluating kidney quality. We closely monitored twenty-two porcine kidneys using 3D PA imaging during a two-hour NMP session. Based on biochemical analyses of perfusate and produced urine, the kidneys were categorized into 'non-functional' and 'functional' groups. Our primary focus was to quantify oxygenation (sO2) within the kidney cortical layer of depths 2 mm, 4 mm, and 6 mm using two-wavelength PA imaging. Next, receiver operating characteristic (ROC) analysis was performed to determine an optimal cortical layer depth and time point for the quantification of sO2 to discriminate between functional and non-functional organs. Finally, for each depth, we assessed the correlation between sO2 and creatinine clearance (CrCl), oxygen consumption (VO2), and renal blood flow (RBF). We found that hypoxia of the renal cortex is associated with poor renal function. In addition, the determination of sO2 within the 2 mm depth of the renal cortex after 30 min of NMP effectively distinguishes between functional and non-functional kidneys. The non-functional kidneys can be detected with the sensitivity and specificity of 80% and 85% respectively, using the cut-off point of sO2 < 39%. Oxygenation significantly correlates with RBF and VO2 in all kidneys. In functional kidneys, sO2 correlated with CrCl, which is not the case for non-functional kidneys. We conclude that the presented technique has a high potential for supporting organ selection for kidney transplantation.

Machine learning of flow cytometry data reveals the delayed innate immune responses correlate with the severity of COVID-19.

Introduction: The COVID-19 pandemic has posed a major burden on healthcare and economic systems across the globe for over 3 years. Even though vaccines are available, the pathogenesis is still unclear. Multiple studies have indicated heterogeneity of immune responses to SARS-CoV-2, and potentially distinct patient immune types that might be related to disease features. However, those conclusions are mainly inferred by comparing the differences of pathological features between moderate and severe patients, some immunological features may be subjectively overlooked. Methods: In this study, the relevance scores(RS), reflecting which features play a more critical role in the decision-making process, between immunological features and the COVID-19 severity are objectively calculated through neural network, where the input features include the immune cell counts and the activation marker concentrations of particular cell, and these quantified characteristic data are robustly generated by processing flow cytometry data sets containing the peripheral blood information of COVID-19 patients through PhenoGraph algorithm. Results: Specifically, the RS between immune cell counts and COVID-19 severity with time indicated that the innate immune responses in severe patients are delayed at the early stage, and the continuous decrease of classical monocytes in peripherial blood is significantly associated with the severity of disease. The RS between activation marker concentrations and COVID-19 severity suggested that the down-regulation of IFN-γ in classical monocytes, Treg, CD8 T cells, and the not down-regulation of IL_17a in classical monocytes, Tregs are highly correlated with the occurrence of severe disease. Finally, a concise dynamic model of immune responses in COVID-19 patients was generalized. Discussion: These results suggest that the delayed innate immune responses in the early stage, and the abnormal expression of IL-17a and IFN-γ in classical monocytes, Tregs, and CD8 T cells are primarily responsible for the severity of COVID-19.

Reduction of metronidazole in municipal wastewater and protection of activated sludge system using a novel immobilized Aspergillus tabacinus LZ-M.

Metronidazole (MNZ) accumulation inhibits municipal wastewater treatment bio-systems, and an effective solution to augment anaerobic activated sludge (AAS) is required. This research discovered that Aspergillus tabacinus LZ-M could degrade 77.39% of MNZ at 5 mg/L. MNZ was metabolized into urea, and the enzymes involved in its degradation were aminotransferase, methyltransferase, monooxygenase, and CN cleavage hydrolase. The strain was immobilized in polyurethane foam and used in AAS for the treatment of MNZ-containing municipal wastewater. The results showed that, using immobilized LZ-M, MNZ was completely removed, and the degradation efficiency of wastewater's chemical oxygen demand (COD) was increased from 11.7% to 83.31%. The extracellular polymer and ROS levels indicated that MNZ's toxicity on AAS was reduced. Furthermore, bioaugmentation stabilized its microbial community, and decreased MNZ resistance genes. These observations confirm that the immobilized fungi are effective in protecting AAS against antibiotic contamination in the treatment process of municipal wastewater.

Transcriptome analysis reveals self-redox mineralization mechanism of azo dyes and novel decolorizing hydrolases in Aspergillus tabacinus LZ-M.

Bio-degradation is the most affordable method of azo dye decontamination, while its drawbacks such as aromatic amines accumulation and low degradation efficiency must be overcome. In this study, a novel mechanism of azo dye degradation by a fungus was discovered. At a concentration of 400 mg/L, the decolorization efficiency of Acid Red 73 (AR73) by Aspergillus tabacinus LZ-M was 90.28%. Metabolite analysis and transcriptome sequencing analysis revealed a self-redox process of AR73 degradation, where the electrons generated in carbon oxidation were transferred to the reduction of -C-N = and -NN. The metabolites, 2-hydroxynaphthalene and N-phenylnitrous amide were mineralized into CO2 through catechol pathway and a glycolytic process. Furthermore, the mineralization ratio of dye was computed to be 31.8% by the carbon balance and electron balance. By using comparative transcriptome, a novel decoloring enzyme Ord95 was discovered in unknown genes through gene cloning. It hydrolyzed AR73 into 2-hydroxynaphthalene and N-phenylnitrous amide, containing a glutathione S-transferase domain with three arginines as key active sites. Here the new mechanism of azo dye degradation was discovered with identification of a novel enzyme in Aspergillus tabacinus LZ-M.

Network pharmacology- and molecular simulation-based exploration of therapeutic targets and mechanisms of heparin for the treatment of sepsis/COVID-19.

Critically infected patients with COVID-19 (coronavirus disease 2019) are prone to develop sepsis-related coagulopathy as a result of a robust immune response. The mechanism underlying the relationship between sepsis and COVID-19 is largely unknown. LMWH (low molecular weight heparin) exhibits both anti-inflammatory and anti-coagulating properties that result in a better prognosis of severely ill patients with COVID-19 co-associated with sepsis-induced coagulopathy or with a higher D-dimer value. Heparin-associated molecular targets and their mechanism of action in sepsis/COVID-19 are not well understood. In this work, we characterize the pharmacological targets, biological functions and therapeutic actions of heparin in sepsis/COVID-19 from the perspective of network pharmacology. A total of 38 potential targets for heparin action against sepsis/COVID-19 and 8 core pharmacological targets were identified, including IL6, KNG1, CXCL8, ALB, VEGFA, F2, IL10 and TNF. Moreover, enrichment analysis showed that heparin could help in treating sepsis/COVID-19 through immunomodulation, inhibition of the inflammatory response, regulation of angiogenesis and antiviral activity. The pharmacological effects of heparin against these targets were further confirmed by molecular docking and simulation analysis, suggesting that heparin exerts effective binding capacity by targeting the essential residues in sepsis/COVID-19. Prospective clinical practice evaluations may consider the use of these key prognostic indicators for the treatment of sepsis/COVID-19.Communicated by Ramaswamy H. Sarma.

Gut microbiome of mealworms (Tenebrio molitor Larvae) show similar responses to polystyrene and corn straw diets.

BACKGROUND: Some insects can degrade both natural and synthetic plastic polymers, their host and gut microbes play crucial roles in this process. However, there is still a scientific gap in understanding how the insect adapted to the polystyrene (PS) diet from natural feed. In this study, we analyzed diet consumption, gut microbiota responses, and metabolic pathways of Tenebrio molitor larvae exposed to PS and corn straw (CS). RESULTS: T. molitor larvae were incubated under controlled conditions (25 ± 1 °C, 75 ± 5% humidity) for 30 days by using PS foam with weight-, number-, and size-average molecular weight (Mw, Mn, and Mz) of 120.0, 73.2, and 150.7 kDa as a diet, respectively. The larvae exhibited lower PS consumption (32.5%) than CS (52.0%), and these diets had no adverse effects on their survival. The gut microbiota structures, metabolic pathways, and enzymatic profiles of PS- and CS-fed larvae showed similar responses. The gut microbiota of larvae analysis indicated Serratia sp., Staphylococcus sp., and Rhodococcus sp. were associated with both PS and CS diets. Metatranscriptomic analysis revealed that xenobiotics, aromatic compounds, and fatty acid degradation pathways were enriched in PS- and CS-fed groups; laccase-like multicopper oxidases, cytochrome P450, monooxygenase, superoxidase, and dehydrogenase were involved in lignin and PS degradation. Furthermore, the upregulated gene lac640 in both PS- and CS-fed groups was overexpressed in E. coli and exhibited PS and lignin degradation ability. CONCLUSIONS: The high similarity of gut microbiomes adapted to biodegradation of PS and CS indicated the plastics-degrading ability of the T. molitor larvae originated through an ancient mechanism that degrades the natural lignocellulose. Video Abstract.

Outflow obstruction after living donor liver transplantation managed with a temporary vena cava filter: A case report.

INTRODUCTION: Outflow obstruction is a rare but critical vascular complication in liver transplantation, which may lead to graft loss and mortality. We report a case of caval vein outflow obstruction due to retrohepatic compression after living donor liver transplantation (LDLT), which was managed by temporary implantation of a vena cava filter. PRESENTATION OF CASE: A 63-year-old male with end stage liver disease presented with caval vein outflow obstruction and massive ascites 12 days after right lobe LDLT. We opted for a minimally invasive approach and implanted a vena cava filter at the compressed site through transjugular route. The patient's ascites drainage significantly decreased and graft function maintained stable after the intervention. On day 50 posttransplant, the filter was successfully removed and the patient was discharged without complications. DISCUSSION: Outflow obstruction after liver transplantation can result from anastomotic stenosis, graft size mismatch, thrombosis or compression of the outflow tract. Various management strategies have been employed both peri- and posttransplant, ranging from surgical interventions to minimally-invasive techniques. The treatment strategy should be tailored to the individual case, considering the timing of presentation and the specific cause for the obstruction. CONCLUSION: We successfully managed a case of compressive outflow obstruction by temporary implantation of a vena cava filter after LDLT. The vena cava filter was safely removed under angiography.

Nickel exposure induces gut microbiome disorder and serum uric acid elevation.

Serum uric acid elevation has been found in long-term nickel (Ni) exposure occupational workers, but the mechanism is unclear. In this study, the relationship between Ni exposure and uric acid elevation was explored in a cohort of 109 participants composed of a Ni-exposed workers group and a control group. The results showed that Ni concentration (5.70 ± 3.21 µg/L) and uric acid level (355.95 ± 67.87 µmol/L) in the serum were increased in the exposure group with a significant positive correlation (r = 0.413, p < 0.0001). The composition of gut microbiota and metabolome revealed that the abundance of uric acid-lowering bacteria, such as Lactobacillus, Lachnospiraceae_Unclassfied and Blautia were reduced while pathogenic bacteria including Parabacteriadies and Escherichia-Shigella were enriched in Ni group, accompanied by impaired intestinal degradation of purines and upregulated biosynthesis of primary bile acids. Consistent with human results, the mice experiments showed that Ni treatment significantly promotes uric acid elevation and systemic inflammation. Lactobacillus and Blautia in gut microbiota were reduced and inflammation-related taxa Alistipes and Mycoplasma were enriched in the Ni treatment. In addition, LC-MS/MS metabolomic analysis indicated that purine nucleosides were accumulated in mice feces, which increased purine absorption and uric acid elevation in the serum. In summary, this study provides evidence that UA elevation was correlated with heavy metals exposure and highlighted the role of gut microbiota in intestinal purine catabolism and in the pathogenesis of heavy metal-induced hyperuricemia.