Hyperbranched Poly-l-Lysine Modified Titanium Surface With Enhanced Osseointegration, Bacteriostasis, and Anti-Inflammatory Properties for Implant Application: An Experimental In Vivo Study.

OBJECTIVES: This study aimed to explore multiple effects of hyperbranched poly-l-lysine (HBPL) titanium (Ti) surfaces on osseointegration, bacteriostasis, and anti-inflammation across three different animal models. METHODS: Ti surfaces were covalently modified with HBPL, with uncoated surfaces as controls. Characterization included scanning electron microscopy (SEM) and surface chemistry and elemental analysis (EDX). Ti and Ti-HBPL implants were placed in conventional canine edentulous sites, post-operative infection canine edentulous sites, and diabetic rat tibias. Implants from canine edentulous models were analyzed using micro-CT and histomorphometry to assess osseointegration at 8 weeks. Post-operative infection beagles were used to evaluate antibacterial efficacy through clinical parameters and bacterial cultures at 1 week. In diabetic rats, micro-CT and histomorphometry were performed at 8 weeks. RESULTS: HBPL was uniformly grafted on Ti-HBPL surfaces. Ti-HBPL surfaces showed higher bone volume/total volume (BV/TV, p < 0.001), bone-implant contact (BIC%, p < 0.001), and trabecular number (Tb.N, p < 0.01) in beagles. Besides, it displayed higher BIC% (p < 0.001) and bone area fraction occupancy (BAFO%, p < 0.01) in hard tissue sections. In an infected model, Ti-HBPL surfaces exhibited lower bleeding on probing (BOP, p < 0.001), and plaque index (DI, p < 0.01), with reduced bacterial colony formation (p < 0.001) compared to the control group. In diabetic rats, Ti-HBPL surfaces showed an increase in BV/TV (p < 0.01) and Tb.N (p < 0.001), downregulated TNF-α and IL-1ß (p < 0.01), and upregulated IL-10 (p < 0.01) and osteocalcin (OCN) expression (p < 0.01). CONCLUSIONS: HBPL-Ti surfaces demonstrated enhanced osseointegration, bacteriostasis, and anti-inflammatory effects in vivo.

Impact of permanganate with polyaluminium chloride on algae-laden karst water: Behaviors and disinfection by-products control.

The removal of algal organic matter (AOM) through water treatment processes is a major approach of reducing the formation of disinfection by-products (DBP). Here, the formation of DBP from AOM in karst water under different combination of potassium permanganate (KMnO4) and polyaluminium chloride (PACl) was investigated. The effect of divalent ions (Ca2+ and Mg2+) on DBP formation was traced by AOM chemistry variations. For DBP formation after KMnO4 preoxidation, total carbonaceous DBPs (C-DBPs) decreased by 12.9% but nitrogen-containing DBPs (N-DBPs) increased by 18.8%. Conversely, the C-DBPs further increased by 3.3% but N-DBPs reduced by 10.7% after the addition of PACl besides KMnO4 preoxidation. The variations of aromatic protein-like, soluble microbial products-like compounds and ultraviolet absorbance at 254 nm (UV254) were highly correlated with the formation of DBPs, which suggest aromatic substances strongly affect DBP behaviors at different treatment conditions. In the presence of divalent ions (Ca2+ = 135.86 mg/L, Mg2+ = 18.51 mg/L), the combination of KMnO4 and PACl was more effective in controlling DBP formation compared to the situation without Ca2+ and Mg2+. Specifically, trichloromethane formation was largely inhibited compared to the other tested DBPs, which may refer to complexation of electron-donating groups via divalent ions. While Ca2+ and Mg2+ may not affect the nature of α-carbon and amine groups, so the variation of haloacetonitriles (HANs) was not obvious. The study enhances the understanding of the DBP formation patterns, transformation of carbon and nitrogen by preoxidation-coagulation (KMnO4-PACl) treatment in algae-laden karst water.

A comprehensive conceptual framework for signaling in-lake CO2 through dissolved organic matter.

Organic carbon (C) and CO2 pools are closely interactive in aquatic environments. While there are strong indications linking freshwater CO2 to dissolved organic matter (DOM), the specific mechanisms underlying their common pathways remain unclear. Here, we present an extensive investigation from 20 subtropical lakes in China, establishing a comprehensive conceptual framework for identifying CO2 drivers and retrieving CO2 magnitude through co-trajectories of DOM evolution. Based on this framework, we show that lake CO2 during wet period is constrained by a combination of biogeochemical processes, while photo-mineralization of activated aromatic compounds fuels CO2 during dry period. We clearly determine that biological degradation of DOM governs temporal variations in CO2 rather than terrestrial C inputs within the subtropical lakes. Specifically, our results identify a shared route for the uptake of atmospheric polycyclic aromatic compounds and CO2 by lakes. Using machine learning, in-lake CO2 levels are well modelled through DOM signaling regardless of varying CO2 mechanisms. This study unravels the mechanistic underpinnings of causal links between lake CO2 and DOM, with important implications for understanding obscure aquatic CO2 drivers amidst the ongoing impacts of global climate change.

Biosustainable Multiscale Transparent Nanocomposite Films for Sensitive Pressure and Humidity Sensors.

Light weight, thinness, transparency, flexibility, and insulation are the key indicators for flexible electronic device substrates. The common flexible substrates are usually polymer materials, but their recycling is an overwhelming challenge. Meanwhile, paper substrates are limited in practical applications because of their poor mechanical and thermal stability. However, natural biomaterials have excellent mechanical properties and versatility thanks to their organic-inorganic multiscale structures, which inspired us to design an organic-inorganic nanocomposite film. For this purpose, a bio-inspired multiscale film was developed using cellulose nanofibers with abundant hydrophilic functional groups to assist in dispersing hydroxyapatite nanowires. The thickness of the biosustainable film is only 40 µm, and it incorporates distinctive mechanical properties (strength: 52.8 MPa; toughness: 0.88 MJ m-3) and excellent optical properties (transmittance: 80.0%; haze: 71.2%). Consequently, this film is optimal as a substrate employed for flexible sensors, which can transmit capacitance and resistance signals through wireless Bluetooth, showing an ultrasensitive response to pressure and humidity (for example, responding to finger pressing with 5000% signal change and exhaled water vapor with 4000% signal change). Therefore, the comprehensive performance of the biomimetic multiscale organic-inorganic composite film confers a prominent prospect in flexible electronics devices, food packaging, and plastic substitution.

Dual-stream multi-dependency graph neural network enables precise cancer survival analysis.

Histopathology image-based survival prediction aims to provide a precise assessment of cancer prognosis and can inform personalized treatment decision-making in order to improve patient outcomes. However, existing methods cannot automatically model the complex correlations between numerous morphologically diverse patches in each whole slide image (WSI), thereby preventing them from achieving a more profound understanding and inference of the patient status. To address this, here we propose a novel deep learning framework, termed dual-stream multi-dependency graph neural network (DM-GNN), to enable precise cancer patient survival analysis. Specifically, DM-GNN is structured with the feature updating and global analysis branches to better model each WSI as two graphs based on morphological affinity and global co-activating dependencies. As these two dependencies depict each WSI from distinct but complementary perspectives, the two designed branches of DM-GNN can jointly achieve the multi-view modeling of complex correlations between the patches. Moreover, DM-GNN is also capable of boosting the utilization of dependency information during graph construction by introducing the affinity-guided attention recalibration module as the readout function. This novel module offers increased robustness against feature perturbation, thereby ensuring more reliable and stable predictions. Extensive benchmarking experiments on five TCGA datasets demonstrate that DM-GNN outperforms other state-of-the-art methods and offers interpretable prediction insights based on the morphological depiction of high-attention patches. Overall, DM-GNN represents a powerful and auxiliary tool for personalized cancer prognosis from histopathology images and has great potential to assist clinicians in making personalized treatment decisions and improving patient outcomes.

Soil bacterial community diversity and life strategy during slope restoration on an uninhabited island: changes under the aggregate spray-seeding technique.

AIMS: We investigated the effects of the aggregate spray-seeding (ASS) technique on soil bacterial community diversity, life strategies, and seasonal change. METHODS AND RESULTS: Soil from six plots with original vegetation (CK, n = 6) was compared to soil from 15 plots with spray-seeding restoration (SR, n = 15) using environmental DNA sequencing. The bacterial Shannon and Chao1 indices of SR soils were significantly greater (P < 0.05) than those of CK soils. The Chao1 index for the SR soil bacterial community was significantly greater in summer (P < 0.05) than in winter. The ratio of the relative abundance of bacterial K-strategists to r-strategists (K/r) and the DNA guanine-cytosine (GC) content in the SR soil were significantly lower (P < 0.05) than those in the CK soil. Principal coordinate analysis revealed significant differences between the SR and CK bacterial communities. The GC content was positively correlated with the K/r ratio. Soil conductivity was negatively associated with the K/r ratio and GC content, indicating that ionic nutrients were closely related to bacterial life strategies. CONCLUSIONS: The ASS technique improved soil bacterial diversity, altered community composition, and favored bacterial r-strategists.

The mediating role of sugar and lipid metabolism and systemic inflammation in the association between breakfast skipping and periodontitis: A population-based study.

BACKGROUND: This study investigated the association between breakfast skipping and periodontitis and the mediating role of sugar and lipid metabolism and systemic inflammation in this association using data from the Korea National Health and Nutrition Examination Survey 2016-2018. METHODS: This study included 11,953 participants, representing an estimated 33.9 million people. Complex sample logistic regression was used to assess the independent association between breakfast skipping and periodontitis. Subgroup analysis was conducted with modifiers including age, sex, body mass index (BMI), urban residence, education level, marital status, and diabetes. Structural equation modeling assessed potential mediation by biomarkers related to glucose and lipid metabolism along with systemic inflammation. RESULTS: The fully adjusted logistic regression model indicated a positive association between breakfast skipping and periodontitis (odds ratio [OR] = 1.234 (1.026-1.483), p = 0.025). This association was highlighted in middle-aged (40-60 years), female, highly educated, married individuals with BMI < 25 kg/m2, and those in urban areas without diabetes. Blood glucose (ß ± SE = 0.006 ± 0.002, p = 0.014), triglycerides (ß ± SE = 0.004 ± 0.002, p = 0.033), and white blood cell count (ß ± SE = 0.011 ± 0.003, p = 0.003) were identified as partial mediators. CONCLUSIONS: A new, independent association between breakfast skipping, and periodontitis has been discovered, which is partially mediated by sugar and lipid metabolism, and systemic inflammation. The findings provide new insights into the benefits of chrononutrition for periodontal health.

BLAM6A-Merge: Leveraging Attention Mechanisms and Feature Fusion Strategies to Improve the Identification of RNA N6-methyladenosine Sites.

RNA N6-methyladenosine is a prevalent and abundant type of RNA modification that exerts significant influence on diverse biological processes. To date, numerous computational approaches have been developed for predicting methylation, with most of them ignoring the correlations of different encoding strategies and failing to explore the adaptability of various attention mechanisms for methylation identification. To solve the above issues, we proposed an innovative framework for predicting RNA m6A modification site, termed BLAM6A-Merge. Specifically, it utilized a multimodal feature fusion strategy to combine the classification results of four features and Blastn tool. Apart from this, different attention mechanisms were employed for extracting higher-level features on specific features after the screening process. Extensive experiments on 12 benchmarking datasets demonstrated that BLAM6A-Merge achieved superior performance (average AUC: 0.849 for the full transcript mode and 0.784 for the mature mRNA mode). Notably, the Blastn tool was employed for the first time in the identification of methylation sites. The data and code can be accessed at https://github.com/DoraemonXia/BLAM6A-Merge.

Radiographic outcomes of lateral sinus floor elevation at sites without perforations and sites with perforations managed with a resorbable membrane: A retrospective study.

AIM: To evaluate the radiographic outcomes of lateral sinus floor elevation with simultaneous implant placement at sites without sinus membrane perforation (SMP) and sites with SMP managed with a resorbable membrane. MATERIALS AND METHODS: One hundred and thirty-nine patients and 170 implants (56 perforation, 114 non-perforation) were included. Cone-beam computed tomography (CBCT) images were taken before surgery (T0), immediately after surgery (T1) and 6 months after surgery (T2). Post-operative augmentation parameters, including endo-sinus bone gain (ESBG) along the implant axis, mean new bone height (NBH) surrounding the implant and augmentation volume (AV), were measured at T1 and T2. RESULTS: At T1, there were no significant differences in ESBG, NBH and AV between the two groups. At T2, although ESBG did not significantly differ between the two groups, NBH (8.50 ± 1.99 mm vs. 9.99 ± 2.52 mm, p = .039) and AV (519.37 ± 258.38 mm3 vs. 700.99 ± 346.53 mm3, p < .001) were significantly lower in the perforation group. The shrinkage of graft material from T1 to T2, including ΔESBG (p = .002), ΔNBH (p < .001) and ΔAV (p < .001), was higher in the perforation group. CONCLUSIONS: SMP during LSFE with simultaneous implant placement is associated with greater resorption of the grafted area at a 6-month follow-up.

Insight into farming native microbiome by bioinoculant in soil-plant system.

Applying beneficial microorganisms (BM) as bioinoculants presents a promising soil-amendment strategy while impacting the native microbiome, which jointly alters soil-plant performance. Leveraging the untapped potential of native microbiomes alongside bioinoculants may enable farmers to sustainably regulate soil-plant systems via natural bioresources. This review synthesizes literature on native microbiome responses to BMs and their interactive effects on soil and plant performance. We highlight that native microbiomes harbor both microbial "helpers" that can improve soil fertility and plant productivity, as well as "inhibitors" that hinder these benefits. To harness the full potential of resident microbiome, it is crucial to elucidate their intricate synergistic and antagonistic interplays with introduced BMs and clarify the conditions that facilitate durable BM-microbiome synergies. Hence, we indicate current challenges in predicting these complex microbial interactions and propose corresponding strategies for microbiome breeding via BM bioinoculant. Overall, fully realizing the potential of BMs requires clarifying their interactions with native soil microbiomes and judiciously engineering microbiome to harness helpful microbes already present within agroecosystems.

Myocardial infarction detection method based on the continuous T-wave area feature and multi-lead-fusion deep features.

Objective.Myocardial infarction (MI) is one of the most threatening cardiovascular diseases. This paper aims to explore a method for using an algorithm to autonomously classify MI based on the electrocardiogram (ECG).Approach.A detection method of MI that fuses continuous T-wave area (C_TWA) feature and ECG deep features is proposed. This method consists of three main parts: (1) The onset of MI is often accompanied by changes in the shape of the T-wave in the ECG, thus the area of the T-wave displayed on different heartbeats will be quite different. The adaptive sliding window method is used to detect the start and end of the T-wave, and calculate the C_TWA on the same ECG record. Additionally, the coefficient of variation of C_TWA is defined as the C_TWA feature of the ECG. (2) The multi lead fusion convolutional neural network was implemented to extract the deep features of the ECG. (3) The C_TWA feature and deep features of the ECG were fused by soft attention, and then inputted into the multi-layer perceptron to obtain the detection result.Main results.According to the inter-patient paradigm, the proposed method reached a 97.67% accuracy, 96.59% precision, and 98.96% recall on the PTB dataset, as well as reached 93.15% accuracy, 93.20% precision, and 95.14% recall on the clinical dataset.Significance.This method accurately extracts the feature of the C_TWA, and combines the deep features of the signal, thereby improving the detection accuracy and achieving favorable results on clinical datasets.

Regulatory networks of senescence-associated gene-transcription factors promote degradation in Moso bamboo shoots.

Bamboo cultivation, particularly Moso bamboo (Phyllostachys edulis), holds significant economic importance in various regions worldwide. Bamboo shoot degradation (BSD) severely affects productivity and economic viability. However, despite its agricultural consequences, the molecular mechanisms underlying BSD remain unclear. Consequently, we explored the dynamic changes of BSD through anatomy, physiology and the transcriptome. Our findings reveal ruptured protoxylem cells, reduced cell wall thickness and the accumulation of sucrose and reactive oxygen species (ROS) during BSD. Transcriptomic analysis underscored the importance of genes related to plant hormone signal transduction, sugar metabolism and ROS homoeostasis in this process. Furthermore, BSD appears to be driven by the coexpression regulatory network of senescence-associated gene transcription factors (SAG-TFs), specifically PeSAG39, PeWRKY22 and PeWRKY75, primarily located in the protoxylem of vascular bundles. Yeast one-hybrid and dual-luciferase assays demonstrated that PeWRKY22 and PeWRKY75 activate PeSAG39 expression by binding to its promoter. This study advanced our understanding of the molecular regulatory mechanisms governing BSD, offering a valuable reference for enhancing Moso bamboo forest productivity.

Effects of returning paddy field to wetland on composition and stability of soil aggregates in the Yellow River Delta.

The composition and stability of soil aggregates are important indicators for measuring soil quality, which would be affected by land use changes. Taking wetlands with different returning years (2 and 15 years) in the Yellow River Delta as the research object, paddy fields and natural wetlands as control, we analyzed the changes in soil physicochemical properties and soil aggregate composition. The results showed that soil water content, total organic carbon, dissolved organic carbon and total phosphorus of the returning soil (0-40 cm) showed an overall increasing trend with returning period, while soil pH and bulk density was in adverse. There was no significant change in clay content, electrical conductivity, and total nitrogen content. The contents of macro-aggregates and micro-aggregates showed overall increasing and decreasing trend with returning period, respectively. The stability of aggregates in the topsoil (0-10 cm) increased with returning years. Geometric mean diameter and mean weight diameter increased by 8.9% and 40.4% in the 15th year of returning, respectively, while the mass proportion of >2.5 mm fraction decreased by 10.5%. There was no effect of returning on aggregates in subsoil (10-40 cm). Our results indicated that returning paddy field to wetland in the Yellow River Delta would play a positive role in improving soil structure and aggregate stability.

TransC-ac4C: Identification of N4-acetylcytidine (ac4C) sites in mRNA using deep learning.

N4-acetylcytidine (ac4C) is a post-transcriptional modification in mRNA that is critical in mRNA translation in terms of stability and regulation. In the past few years, numerous approaches employing convolutional neural networks (CNN) and Transformer have been proposed for the identification of ac4C sites, with each variety of approaches processing distinct characteristics. CNN-based methods excels at extracting local features and positional information, whereas Transformer-based ones stands out in establishing long-range dependencies and generating global representations. Given the importance of both local and global features in mRNA ac4C sites identification, we propose a novel method termed TransC-ac4C which combines CNN and Transformer together for enhancing the feature extraction capability and improving the identification accuracy. Five different feature encoding strategies (One-hot, NCP, ND, EIIP, and K-mer) are employed to generate the mRNA sequence representations, in which way the sequence attributes and physical and chemical properties of the sequences can be embedded. To strengthen the relevance of features, we construct a novel feature fusion method. Firstly, the CNN is employed to process five single features, stitch them together and feed them to the Transformer layer. Then, our approach employs CNN to extract local features and Transformer subsequently to establish global long-range dependencies among extracted features. We use 5-fold cross-validation to evaluate the model, and the evaluation indicators are significantly improved. The prediction accuracy of the two datasets is as high as 81.42.

Effects of tidal hydrology on soil phosphorus forms in the Yellow River estuary wetland: A field study of soil core translocation.

Phosphorus (P) forms in soil are related to the P cycle and play an important role in maintaining the productivity and function of wetlands. Tidal hydrology is a key factor controlling soil P forms in estuary wetlands; however, the response of soil P forms to tidal hydrological changes remains unclear. A translocation experiment in the Yellow River Estuary wetland was conducted to study the effect of hydrological changes on P forms in the soil, in which freshwater marsh soils in the supratidal zone were translocated to salt marshes in different intertidal zones (up-high-tidal zone, high-tidal zone, and middle-tidal zone). Over a 23-month experiment, soil properties showed varying changes under different tidal hydrology conditions, with an increase in pH, salinity, Ca2+ and salt ions and a decrease in iron oxide and nutrients. Compared with the control, the content of different forms of phosphorus (total phosphorus, inorganic phosphorus, organic phosphorus, and calcium-bound phosphorus) in the cultured soil cores decreased from 3.3 % to 67.0 % in the intertidal zones, whereas the content of ferrum­aluminum-bound phosphorus increased from 58.9 % to 65.1 % at the end of the experiment. According to the partial least squares structural equation model, P forms are influenced by tidal hydrology mainly through the mediation of salt ions and nutrient levels. These results suggest that seawater intrusion promotes the release of P in the supratidal zone soil of estuary wetlands.

Algae removal and algal organic matter chemistry modulated by KMnO4-PAC in simulated karst water.

In this study, we examined the modulation of algae removal and algal organic matter (AOM) chemistry by potassium permanganate and poly-aluminum chloride (KMnO4-PAC) in simulated karst water. Specifically, we verified the compositional changes of AOM sourcing from Chlorella sp. and Pseudanabaena sp. in response to the presence of divalent ions (Ca2+ and Mg2+). Aromatic protein and soluble microbial products were identified as the primary AOM components. Divalent ions accelerated dissolved organic carbon (DOC) and UV254 removal, particularly with Pseudanabaena sp. greater than Chlorella sp. (P < 0.05). Surface morphology analysis manifested that the removal of filamentous Pseudanabaena sp. was more feasible in comparison to globular Chlorella sp.. Our results highlight the significance of divalent ions in governing chemical behaviors and subsequent removal of both algae and AOM. This study upscales the understanding of the interactions among divalent ions, algae and AOM during preoxidation and coagulation process in algae-laden karst water.

GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction.

The application of Artificial Intelligence (AI) to screen drug molecules with potential therapeutic effects has revolutionized the drug discovery process, with significantly lower economic cost and time consumption than the traditional drug discovery pipeline. With the great power of AI, it is possible to rapidly search the vast chemical space for potential drug-target interactions (DTIs) between candidate drug molecules and disease protein targets. However, only a small proportion of molecules have labelled DTIs, consequently limiting the performance of AI-based drug screening. To solve this problem, a machine learning-based approach with great ability to generalize DTI prediction across molecules is desirable. Many existing machine learning approaches for DTI identification failed to exploit the full information with respect to the topological structures of candidate molecules. To develop a better approach for DTI prediction, we propose GraphormerDTI, which employs the powerful Graph Transformer neural network to model molecular structures. GraphormerDTI embeds molecular graphs into vector-format representations through iterative Transformer-based message passing, which encodes molecules' structural characteristics by node centrality encoding, node spatial encoding and edge encoding. With a strong structural inductive bias, the proposed GraphormerDTI approach can effectively infer informative representations for out-of-sample molecules and as such, it is capable of predicting DTIs across molecules with an exceptional performance. GraphormerDTI integrates the Graph Transformer neural network with a 1-dimensional Convolutional Neural Network (1D-CNN) to extract the drugs' and target proteins' representations and leverages an attention mechanism to model the interactions between them. To examine GraphormerDTI's performance for DTI prediction, we conduct experiments on three benchmark datasets, where GraphormerDTI achieves a superior performance than five state-of-the-art baselines for out-of-molecule DTI prediction, including GNN-CPI, GNN-PT, DeepEmbedding-DTI, MolTrans and HyperAttentionDTI, and is on a par with the best baseline for transductive DTI prediction. The source codes and datasets are publicly accessible at https://github.com/mengmeng34/GraphormerDTI.

Effect of oxide interactions on chromium speciation transformation during simulated municipal solid waste incineration.

Chromium released during municipal solid waste incineration (MSWI) is toxic and carcinogenic. The removal of chromium from simulated MSWI flue gas by four sorbents (CaO, bamboo charcoal (BC), powdered activated carbon (PAC), and Al2O3) and the effects of four oxides (SiO2, Al2O3, Fe2O3, and CaO) on chromium speciation transformation were investigated. The results showed that the removal rates of total Cr by the four sorbents were Al2O3 < CaO < PAC < BC, while the removal rates of Cr(VI) by the four sorbents were Al2O3 < PAC < BC < CaO. CaO had a strong oxidizing effect on Cr(III), while BC and PAC had a better-reducing effect on Cr(VI). SiO2 was better for the reduction of Na2CrO4 and K2CrO4 above 1000°C due to its strong acidity, and the addition of CaO significantly inhibited the reduction of Cr(VI). MgCrO4 decomposed above 700°C to form MgCr2O4, and the reaction between MgCrO4 and oxides also existed in the form of a more stable trivalent spinel. Furthermore, when investigating the effect of oxides on the oxidation of Cr(III) in CrCl3, it was discovered that CaO promoted the conversion of Cr(III) to Cr(VI), while the presence of chlorine caused chromium to exist in the form of Cr(V), and increasing the content of CaO and extending the heating time facilitated the oxidation of Cr(III). In addition, silicate, aluminate, and ferrite were generated after the addition of SiO2, Al2O3, and Fe2O3, which reduced the alkalinity of CaO and had an important role in inhibiting the oxidation of Cr(III). The acidic oxides can not only promote the reduction of Cr(VI) but also have an inhibitory effect on the oxidation of Cr(III) ascribed to alkali metals/alkaline earth metals, and the proportion of acidic oxides can be increased moderately to reduce the generation of harmful substances in the hazardous solid waste heat treatment.

Cone-beam computed tomographic evaluation of mandibular incisor alveolar bone changes for the intrusion arch technique: A retrospective cohort research.

Objective: : Alveolar bone loss is a common adverse effect of intrusion treatment. Mandibular incisors are prone to dehiscence and fenestrations as they suffer from thinner alveolar bone thickness. Methods: : Thirty skeletal class II patients treated with mandibular intrusion arch therapy were included in this study. Lateral cephalograms and cone-beam computed tomography images were taken before treatment (T1) and immediately after intrusion arch removal (T2) to evaluate the tooth displacement and the alveolar bone changes. Pearson's and Spearman's correlation was used to identify risk factors of alveolar bone loss during the intrusion treatment. Results: : Deep overbite was successfully corrected (P < 0.05), accompanied by mandibular incisor proclination (P < 0.05). There were no statistically significant change in the true incisor intrusion (P > 0.05). The labial and lingual vertical alveolar bone levels showed a significant decrease (P < 0.05). The alveolar bone is thinning in the labial crestal area and lingual apical area (P < 0.05); accompanied by thickening in the labial apical area (P < 0.05). Proclined incisors, non-extraction treatment, and increased A point-nasion-B point (ANB) degree were positively correlated with alveolar bone loss. Conclusions: : While the mandibular intrusion arch effectively corrected the deep overbite, it did cause some unwanted incisor labial tipping/flaring. During the intrusion treatment, the alveolar bone underwent corresponding changes, which was thinning in the labial crestal area and thickening in the labial apical area vice versa. And increased axis change of incisors, non-extraction treatment, and increased ANB were identified as risk factors for alveolar bone loss in patients with mandibular intrusion therapy.

PmxPred: A data-driven approach for the identification of active polymyxin analogues against gram-negative bacteria.

The multidrug-resistant Gram-negative bacteria has evolved into a worldwide threat to human health; over recent decades, polymyxins have re-emerged in clinical practice due to their high activity against multidrug-resistant bacteria. Nevertheless, the nephrotoxicity and neurotoxicity of polymyxins seriously hinder their practical use in the clinic. Based on the quantitative structure-activity relationship (QSAR), analogue design is an efficient strategy for discovering biologically active compounds with fewer adverse effects. To accelerate the polymyxin analogues discovery process and find the polymyxin analogues with high antimicrobial activity against Gram-negative bacteria, here we developed PmxPred, a GCN and catBoost-based machine learning framework. The RDKit descriptors were used for the molecule and residues representation, and the ensemble learning model was utilized for the antimicrobial activity prediction. This framework was trained and evaluated on multiple Gram-negative bacteria datasets, including Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and a general Gram-negative bacteria dataset achieving an AUROC of 0.857, 0.880, 0.756, 0.895 and 0.865 on the independent test, respectively. PmxPred outperformed the transfer learning method that trained on 10 million molecules. We interpreted our model well-trained model by analysing the importance of global and residue features. Overall, PmxPred provides a powerful additional tool for predicting active polymyxin analogues, and holds the potential elucidate the mechanisms underlying the antimicrobial activity of polymyxins. The source code is publicly available on GitHub (https://github.com/yanwu20/PmxPred).